rm(list=ls())
gc()
setwd("/hpc/group/pbenfeylab/CheWei/CW_data/genesys/")

A matrix: 2 x 6 of type dbl

	used	(Mb)	gc trigger	(Mb)	max used	(Mb)
Ncells	624160	33.4	1358941	72.6	1335971	71.4
Vcells	1157075	8.9	8388608	64.0	1802277	13.8

## Need seu4
suppressMessages(library(Seurat))
suppressMessages(library(cowplot))
suppressMessages(library(scattermore))
suppressMessages(library(scater))
suppressMessages(library(cowplot))
suppressMessages(library(RColorBrewer))
suppressMessages(library(grid))
suppressMessages(library(gplots))
suppressMessages(library(circular))
suppressMessages(library(ggplot2))
suppressMessages(library(ggnewscale))
suppressMessages(library(tidyverse))
suppressMessages(library(ComplexHeatmap))
suppressMessages(library(circlize))
suppressMessages(library(patchwork))

Warning message:
“package ‘ggplot2’ was built under R version 4.2.3”
Warning message:
“package ‘dplyr’ was built under R version 4.2.3”

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Stream 8

Matrix products: default
BLAS/LAPACK: /hpc/group/pbenfeylab/ch416/miniconda3/envs/seu4/lib/libopenblasp-r0.3.21.so

locale:
 [1] LC_CTYPE=C.UTF-8           LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] grid      stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] patchwork_1.1.2             circlize_0.4.15            
 [3] ComplexHeatmap_2.14.0       forcats_0.5.2              
 [5] stringr_1.5.0               dplyr_1.1.1                
 [7] purrr_1.0.1                 readr_2.1.3                
 [9] tidyr_1.3.0                 tibble_3.2.1               
[11] tidyverse_1.3.2             ggnewscale_0.4.8           
[13] circular_0.4-95             gplots_3.1.3               
[15] RColorBrewer_1.1-3          scater_1.26.0              
[17] ggplot2_3.4.2               scuttle_1.8.0              
[19] SingleCellExperiment_1.20.0 SummarizedExperiment_1.28.0
[21] Biobase_2.58.0              GenomicRanges_1.50.0       
[23] GenomeInfoDb_1.34.8         IRanges_2.32.0             
[25] S4Vectors_0.36.0            BiocGenerics_0.44.0        
[27] MatrixGenerics_1.10.0       matrixStats_0.63.0         
[29] scattermore_0.8             cowplot_1.1.1              
[31] SeuratObject_4.1.3          Seurat_4.1.1.9001          

loaded via a namespace (and not attached):
  [1] utf8_1.2.3                spatstat.explore_3.1-0   
  [3] reticulate_1.28           tidyselect_1.2.0         
  [5] htmlwidgets_1.6.2         BiocParallel_1.32.5      
  [7] Rtsne_0.16                munsell_0.5.0            
  [9] ScaledMatrix_1.6.0        codetools_0.2-19         
 [11] ica_1.0-3                 pbdZMQ_0.3-8             
 [13] future_1.32.0             miniUI_0.1.1.1           
 [15] withr_2.5.0               spatstat.random_3.1-4    
 [17] colorspace_2.1-0          progressr_0.13.0         
 [19] uuid_1.1-0                ROCR_1.0-11              
 [21] tensor_1.5                listenv_0.9.0            
 [23] repr_1.1.4                GenomeInfoDbData_1.2.9   
 [25] polyclip_1.10-4           parallelly_1.35.0        
 [27] vctrs_0.6.2               generics_0.1.3           
 [29] timechange_0.1.1          doParallel_1.0.17        
 [31] R6_2.5.1                  clue_0.3-64              
 [33] ggbeeswarm_0.7.1          rsvd_1.0.5               
 [35] bitops_1.0-7              spatstat.utils_3.0-2     
 [37] DelayedArray_0.24.0       assertthat_0.2.1         
 [39] promises_1.2.0.1          scales_1.2.1             
 [41] googlesheets4_1.0.1       beeswarm_0.4.0           
 [43] gtable_0.3.3              beachmat_2.14.0          
 [45] globals_0.16.2            goftest_1.2-3            
 [47] rlang_1.1.0               GlobalOptions_0.1.2      
 [49] splines_4.2.2             lazyeval_0.2.2           
 [51] gargle_1.2.1              spatstat.geom_3.1-0      
 [53] broom_1.0.2               modelr_0.1.10            
 [55] reshape2_1.4.4            abind_1.4-5              
 [57] backports_1.4.1           httpuv_1.6.9             
 [59] tools_4.2.2               ellipsis_0.3.2           
 [61] ggridges_0.5.4            Rcpp_1.0.10              
 [63] plyr_1.8.8                base64enc_0.1-3          
 [65] sparseMatrixStats_1.10.0  zlibbioc_1.44.0          
 [67] RCurl_1.98-1.6            deldir_1.0-6             
 [69] GetoptLong_1.0.5          pbapply_1.7-0            
 [71] viridis_0.6.2             zoo_1.8-12               
 [73] haven_2.5.1               ggrepel_0.9.3            
 [75] cluster_2.1.4             fs_1.6.1                 
 [77] magrittr_2.0.3            data.table_1.14.8        
 [79] RSpectra_0.16-1           reprex_2.0.2             
 [81] lmtest_0.9-40             RANN_2.6.1               
 [83] googledrive_2.0.0         mvtnorm_1.1-3            
 [85] fitdistrplus_1.1-8        hms_1.1.2                
 [87] mime_0.12                 evaluate_0.20            
 [89] xtable_1.8-4              readxl_1.4.1             
 [91] shape_1.4.6               fastDummies_1.6.3        
 [93] gridExtra_2.3             compiler_4.2.2           
 [95] KernSmooth_2.23-20        crayon_1.5.2             
 [97] htmltools_0.5.5           tzdb_0.3.0               
 [99] later_1.3.0               lubridate_1.9.0          
[101] DBI_1.1.3                 dbplyr_2.2.1             
[103] MASS_7.3-58.3             boot_1.3-28.1            
[105] Matrix_1.5-4              cli_3.6.1                
[107] parallel_4.2.2            igraph_1.4.2             
[109] pkgconfig_2.0.3           sp_1.6-0                 
[111] IRdisplay_1.1             plotly_4.10.1            
[113] spatstat.sparse_3.0-1     foreach_1.5.2            
[115] xml2_1.3.3                vipor_0.4.5              
[117] XVector_0.38.0            rvest_1.0.3              
[119] digest_0.6.31             sctransform_0.3.5        
[121] RcppAnnoy_0.0.20          spatstat.data_3.0-1      
[123] cellranger_1.1.0          leiden_0.4.3             
[125] uwot_0.1.14               DelayedMatrixStats_1.20.0
[127] shiny_1.7.4               gtools_3.9.4             
[129] rjson_0.2.21              lifecycle_1.0.3          
[131] nlme_3.1-162              jsonlite_1.8.4           
[133] BiocNeighbors_1.16.0      viridisLite_0.4.1        
[135] fansi_1.0.4               pillar_1.9.0             
[137] lattice_0.21-8            fastmap_1.1.1            
[139] httr_1.4.5                survival_3.4-0           
[141] glue_1.6.2                iterators_1.0.14         
[143] png_0.1-8                 stringi_1.7.12           
[145] RcppHNSW_0.4.1            BiocSingular_1.14.0      
[147] caTools_1.18.2            IRkernel_1.3.1.9000      
[149] irlba_2.3.5.1             future.apply_1.10.0      

Load TF list

wanted_TFs <- read.csv("./Kay_TF_thalemine_annotations.csv")

nrow(wanted_TFs)

2484

## Make TF names unique
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G33880"]="WOX9"
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G45160"]="SCL27"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G04410"]="NAC78"
wanted_TFs$Name[wanted_TFs$GeneID=="AT3G29035"]="ORS1"
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G02540"]="ZHD3"
wanted_TFs$Name[wanted_TFs$GeneID=="AT3G16500"]="IAA26"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G09740"]="HAG5"
wanted_TFs$Name[wanted_TFs$GeneID=="AT4G24660"]="ZHD2"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G46880"]="HDG5"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G28420"]="RLT1"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G14580"]="BLJ"
wanted_TFs$Name[wanted_TFs$GeneID=="AT3G45260"]="BIB"
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G02070"]="RVN"
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G28160"]="FIT"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G68360"]="GIS3"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G20640"]="NLP4"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G05550"]="VFP5"
wanted_TFs$Name[wanted_TFs$GeneID=="AT3G59470"]="FRF1"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G15150"]="HAT7"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G14750"]="WER"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G75710"]="BRON"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G74500"]="TMO7"
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G12646"]="RITF1"
wanted_TFs$Name[wanted_TFs$GeneID=="AT3G48100"]="ARR5"
wanted_TFs$Name[wanted_TFs$GeneID=="AT4G16141"]="GATA17L"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G65640"]="NFL"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G62700"]="VND5"
wanted_TFs$Name[wanted_TFs$GeneID=="AT4G36160"]="VND2"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G66300"]="VND3"
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G12260"]="VND4"
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G62380"]="VND6"

## TTG1
wanted_TFs$Name[wanted_TFs$GeneID=="AT5G24520"]

'TTG1'

## SCRAMBLED
wanted_TFs$Name[wanted_TFs$GeneID=="AT1G11130"]

## CAPRICE
wanted_TFs$Name[wanted_TFs$GeneID=="AT2G46410"]

'CPC'

Load GRN centrality scores for each transition

stem2pro <- read.csv("./TF_GRN_centrality_t0-t1_zscore3.csv")
pro2trans <- read.csv("./TF_GRN_centrality_t1-t3_zscore3.csv")
trans2el <- read.csv("./TF_GRN_centrality_t3-t5_zscore3.csv")
el2el <- read.csv("./TF_GRN_centrality_t5-t7_zscore3.csv")
el2mat <- read.csv("./TF_GRN_centrality_t7-t9_zscore3.csv")

head(stem2pro)

A data.frame: 6 x 62

	X	tf_occurance	atri_degree_centrality	atri_out_centrality	atri_in_centrality	atri_betweenness_centrality	atri_closeness_centrality	atri_eigenvector_centrality	tri_degree_centrality	tri_out_centrality	...	phl_in_centrality	phl_betweenness_centrality	phl_closeness_centrality	phl_eigenvector_centrality	col_degree_centrality	col_out_centrality	col_in_centrality	col_betweenness_centrality	col_closeness_centrality	col_eigenvector_centrality
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	...	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	GAMMA-H2AX	10	0.8986486	0.3445946	0.55405405	0.824314156	0.001607054	0.11806416	0.80544747	0.12451362	...	0.1495017	0.248917916	0.0011086941	0.06175429	0.07805907	0.02742616	0.05063291	0.31334238	0.0003917786	0.03439140
2	CRF2	9	0.9729730	0.4436937	0.52927928	0.987777846	0.001552485	0.12188729	1.17898833	0.73929961	...	0.3172757	0.630057324	0.0013108166	0.07501625	0.79746835	0.33438819	0.46308017	0.93998481	0.0005974520	0.11139614
3	HMGB6	9	0.2612613	0.2049550	0.05630631	0.002308177	0.001400878	0.06217343	0.45136187	0.28015564	...	0.1029900	0.005635679	0.0009932707	0.04870514	0.03375527	0.01582278	0.01793249	0.00000000	0.0003046752	0.01628732
4	HAT1	9	0.4054054	0.2162162	0.18918919	0.002852175	0.001266005	0.07435692	0.05058366	0.04669261	...	0.4302326	0.967158833	0.0012671678	0.08334429	0.48206751	0.24156118	0.24050633	0.02677119	0.0005415002	0.09398170
5	GRP2B	9	0.2702703	0.1238739	0.14639640	0.038954304	0.001162324	0.06130034	0.00000000	0.00000000	...	0.5332226	0.966937717	0.0012946771	0.08259318	0.67299578	0.41561181	0.25738397	0.28124346	0.0005718634	0.10969489
6	HB-2	9	0.5045045	0.3243243	0.18018018	0.621901247	0.001388507	0.08859595	0.04669261	0.01167315	...	0.3039867	0.856015169	0.0013859050	0.08585141	0.54852321	0.13502110	0.41350211	0.01482474	0.0005300467	0.10354056

min_max_normalize <- function(data) {
  min_val <- min(data)
  max_val <- max(data)
  normalized_data <- (data - min_val) / (max_val - min_val)
  return(normalized_data)
}

summary(stem2pro$tri_betweenness_centrality)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.0236  0.0000  0.9904 

summary(min_max_normalize(stem2pro$tri_betweenness_centrality))

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.02383 0.00000 1.00000 

ncol(stem2pro)

62

stem2pro <- as.data.frame(cbind(stem2pro[,1],apply(stem2pro[,3:62],2,min_max_normalize)))
pro2trans <- as.data.frame(cbind(pro2trans[,1],apply(pro2trans[,3:62],2,min_max_normalize)))
trans2el <- as.data.frame(cbind(trans2el[,1],apply(trans2el[,3:62],2,min_max_normalize)))
el2el <- as.data.frame(cbind(el2el[,1],apply(el2el[,3:62],2,min_max_normalize)))
el2mat <- as.data.frame(cbind(el2mat[,1],apply(el2mat[,3:62],2,min_max_normalize)))

dat <- stem2pro %>%
left_join(pro2trans, by = "V1") %>%
left_join(trans2el, by = "V1") %>%
left_join(el2el, by = "V1") %>%
left_join(el2mat, by = "V1")

dat[is.na(dat)] <- 0

n <- c('atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality',
       'tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality',
       'lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality',
       'cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality',
       'end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality',
       'per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality',
       'pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality',
       'xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality',
       'phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality',
       'col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality')

colnames(dat) <- c("TF",gsub("$","_1",n), gsub("$","_2",n),gsub("$","_3",n),gsub("$","_4",n),gsub("$","_5",n))

GeneID <- wanted_TFs$GeneID[match(dat$TF, wanted_TFs$Name)]

dat <- cbind(GeneID, dat)

head(dat)

A data.frame: 6 x 302

	GeneID	TF	atri_degree_centrality_1	atri_out_centrality_1	atri_in_centrality_1	atri_betweenness_centrality_1	atri_closeness_centrality_1	atri_eigenvector_centrality_1	tri_degree_centrality_1	tri_out_centrality_1	...	phl_in_centrality_5	phl_betweenness_centrality_5	phl_closeness_centrality_5	phl_eigenvector_centrality_5	col_degree_centrality_5	col_out_centrality_5	col_in_centrality_5	col_betweenness_centrality_5	col_closeness_centrality_5	col_eigenvector_centrality_5
	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	...	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>
1	AT1G54690	GAMMA-H2AX	0.776264591439689	0.495145631067961	0.763975155279503	0.834513711603393	0.940151233514354	0.954898795508496	0.63302752293578	0.135593220338983	...	0.00353356890459364	0	0.433778629760381	0.0384080822193205	0	0	0	0	0	0
2	AT4G23750	CRF2	0.840466926070039	0.637540453074434	0.729813664596273	1	0.90822754077444	0.985820114506607	0.926605504587156	0.805084745762712	...	0.0117785630153121	0.0001122768497611	0.579606861231972	0.117223661689331	0	0	0	0	0	0
3	AT5G23420	HMGB6	0.22568093385214	0.294498381877023	0.077639751552795	0.0023367372148563	0.819534761406972	0.502856548739284	0.35474006116208	0.305084745762712	...	0	0	0	0	0	0	0	0	0	0
4	AT4G17460	HAT1	0.350194552529183	0.310679611650485	0.260869565217391	0.00288746602981141	0.740632037115667	0.601396170660393	0.0397553516819572	0.0508474576271186	...	0.00235571260306243	0	0.462623504191286	0.0384080822193205	0.00424088210347752	0.00672268907563025	0.00171232876712329	0	0.534086128307187	0.0342094681539202
5	AT2G21060	GRP2B	0.233463035019455	0.177993527508091	0.201863354037267	0.0394363007494029	0.67997746133997	0.495794974854396	0	0	...	0.156654888103651	0.00484869654894231	0.874134371864073	0.556195610463054	0.00508905852417303	0.00672268907563025	0.00342465753424658	0	0.474466815605091	0.0339845056831895
6	AT4G16780	HB-2	0.43579766536965	0.466019417475728	0.248447204968944	0.629596269455653	0.81229784641811	0.716560956988195	0.036697247706422	0.0127118644067797	...	0.00117785630153121	0	0.398777242871396	0.0289197059513674	0	0	0	0	0	0

numz <- function(x){
    sum(x==0)/length(x)
}

dat$combined_score <- min_max_normalize(rowSums(apply(dat[,grep("centrality",colnames(dat))],2,as.numeric)))
dat$celltype_specificity <- min_max_normalize(apply(apply(dat[,grep("centrality",colnames(dat))],2,as.numeric),1,numz))
dat$weighted_score <- dat$combined_score + dat$celltype_specificity
dat <- dat %>% arrange(desc(weighted_score))

head(dat)

A data.frame: 6 x 305

	GeneID	TF	atri_degree_centrality_1	atri_out_centrality_1	atri_in_centrality_1	atri_betweenness_centrality_1	atri_closeness_centrality_1	atri_eigenvector_centrality_1	tri_degree_centrality_1	tri_out_centrality_1	...	phl_eigenvector_centrality_5	col_degree_centrality_5	col_out_centrality_5	col_in_centrality_5	col_betweenness_centrality_5	col_closeness_centrality_5	col_eigenvector_centrality_5	combined_score	celltype_specificity	weighted_score
	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	...	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>
1	AT5G24800	BZIP9	0	0	0	0	0	0	0	0	...	0.991229312680569	0	0	0	0	0	0	0.7370916	0.6000000	1.337092
2	AT3G43430	AT3G43430	0	0	0	0	0	0	0	0	...	0.481531356662094	0	0	0	0	0	0	0.7528181	0.4978723	1.250690
3	AT5G15150	HAT7	0	0	0	0	0	0	0.253822629969419	0.038135593220339	...	0	0	0	0	0	0	0	0.7999175	0.4425532	1.242471
4	AT3G20840	PLT1	0.961089494163424	0.970873786407767	0.602484472049689	0.976313513958659	0.914746192175766	0.986144172298438	0	0	...	0	0.0398642917726887	0.0756302521008403	0.00342465753424658	0	0.745449540294179	0.250685880776617	0.5516755	0.6680851	1.219761
5	AT5G57620	MYB36	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0.4791306	0.7148936	1.194024
6	AT2G45050	GATA2	1	0.828478964401295	0.801242236024845	0.998594869472124	0.965657213462023	1	0.981651376146789	0.936440677966102	...	0	0	0	0	0	0	0	0.7860795	0.4042553	1.190335

write.csv(dat,"TF_GRN_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=FALSE)

GO terms (Prepare GS)

gene_list <- read.table('./gene_list_1108.csv', sep=",", header = TRUE)

exptf <- intersect(gene_list$features, wanted_TFs$GeneID)
length(exptf)

1495

sfun <- read.csv('string_functional_annotations.tsv', sep="\t", header=TRUE)
sann <- read.csv('string_protein_annotations.tsv', sep="\t", header=TRUE)

gsgo1 <- unique(sfun[grep('root|xylem|phloem|procambium|pericycle|vascular|vasculature|stele|tracheary|sieve|trichoblast|atrichoblast|epidermis|epidermal tissue|lateral root cap|root hair|trichome|cortex|endodermis|ground tissue|columella|quiescent center',sfun$term.description, ignore.case=TRUE),]$X.node)
gsgo2 <- unique(sann[grep('root|xylem|phloem|procambium|pericycle|vascular|vasculature|stele|tracheary|sieve|trichoblast|atrichoblast|epidermis|epidermal tissue|lateral root cap|root hair|trichome|cortex|endodermis|ground tissue|columella|quiescent center',sann$domain_summary_url, ignore.case=TRUE),]$X.node)
gsgo <- sort(unique(c(gsgo1, gsgo2)))

#write.csv(data.frame(GeneID=gsgo),"./Gold_Standard_Root_TF_StringDB.csv", quote=FALSE, row.names=FALSE)

length(gsgo)
gsgo

209

1. 'A0A1I9LNX5'
2. 'AGL12'
3. 'AGL16'
4. 'AHK2'
5. 'AHK3'
6. 'AHK4'
7. 'AIL5'
8. 'AL6'
9. 'ANL2'
10. 'APL'
11. 'ARF10'
12. 'ARF16'
13. 'ARF17'
14. 'ARF19'
15. 'ARF7'
16. 'ARR1'
17. 'ARR10'
18. 'ARR12'
19. 'ARR2'
20. 'ATHB-13'
21. 'ATHB-15'
22. 'ATHB-23'
23. 'ATHB-8'
24. 'ATHB-X'
25. 'ATX1-2'
26. 'BEH1'
27. 'BHLH12'
28. 'BHLH155'
29. 'BHLH2'
30. 'BHLH32'
31. 'BHLH48'
32. 'BHLH54'
33. 'BHLH66'
34. 'BHLH69'
35. 'BHLH82'
36. 'BHLH83'
37. 'BHLH85'
38. 'BHLH86'
39. 'BIB'
40. 'BRM'
41. 'BZIP19'
42. 'BZIP23'
43. 'BZR2'
44. 'CHC1-2'
45. 'CHR12'
46. 'CHR23'
47. 'CPC'
48. 'CRF2'
49. 'DAR2'
50. 'DOF1.1'
51. 'DOF2.2'
52. 'DOF2.4'
53. 'DOF3.2'
54. 'DOF3.6'
55. 'DOF5.1'
56. 'DOF5.3'
57. 'DOF5.6'
58. 'DOT2'
59. 'DPB'
60. 'E2FB'
61. 'EFM'
62. 'ERF055'
63. 'ERF104'
64. 'ET2'
65. 'ETC1'
66. 'ETR1'
67. 'F13K3.12'
68. 'F24O1.3'
69. 'F27L4.4'
70. 'F2I11.230'
71. 'FEZ'
72. 'GAI'
73. 'GALT2'
74. 'GATA12'
75. 'GATA23'
76. 'GIF1'
77. 'GIS3'
78. 'GL2'
79. 'GL3'
80. 'GRF2-2'
81. 'GRF3-2'
82. 'GTL1'
83. 'HAG1'
84. 'HAT2'
85. 'HAT4'
86. 'HDG11'
87. 'HDG12'
88. 'HDG2'
89. 'HDT1'
90. 'HHO1'
91. 'HHO2'
92. 'HHO3'
93. 'HHO6'
94. 'HRS1'
95. 'HSFA4C'
96. 'IAA12'
97. 'IAA14'
98. 'IAA20'
99. 'IAA28'
100. 'IAA3'
101. 'IAA31'
102. 'IAA8'
103. 'IDD4'
104. 'IDD6'
105. 'IWS1'
106. 'JKD'
107. 'JMJ14'
108. 'JMJ25'
109. 'JMJ30'
110. 'JMJ32'
111. 'K15E6.5'
112. 'KAN1'
113. 'KNAT7'
114. 'KUA1'
115. 'LBD15'
116. 'LBD16'
117. 'LBD18'
118. 'LBD29'
119. 'LBD3'
120. 'LBD30'
121. 'LBD4'
122. 'LDL1'
123. 'LEP'
124. 'LHW'
125. 'LRP1'
126. 'MAMYB'
127. 'MGP'
128. 'MIF1'
129. 'MTERF9'
130. 'MYB12'
131. 'MYB124'
132. 'MYB20'
133. 'MYB23'
134. 'MYB32'
135. 'MYB36'
136. 'MYB43'
137. 'MYB46'
138. 'MYB52'
139. 'MYB60'
140. 'MYB61'
141. 'MYB7'
142. 'MYB83'
143. 'MYB85'
144. 'MYB86'
145. 'MYB88'
146. 'MYC3'
147. 'MYC4'
148. 'NAA10'
149. 'NAC003'
150. 'NAC005'
151. 'NAC007'
152. 'NAC010'
153. 'NAC026'
154. 'NAC030'
155. 'NAC037'
156. 'NAC043'
157. 'NAC045'
158. 'NAC056'
159. 'NAC075'
160. 'NAC076'
161. 'NAC083'
162. 'NAC084'
163. 'NAC086'
164. 'NAC101'
165. 'NAC104'
166. 'NAC105'
167. 'NAC92'
168. 'NUC'
169. 'PKL'
170. 'PLT1'
171. 'PLT2'
172. 'PRE3'
173. 'Q1G3Q4_ARATH'
174. 'RAP2-11'
175. 'RAP2-12'
176. 'RAV1'
177. 'RBR1'
178. 'REF6'
179. 'REV'
180. 'RGA'
181. 'SCL22'
182. 'SCL27'
183. 'SCL6'
184. 'SCR'
185. 'SCRM'
186. 'SHR'
187. 'SMB'
188. 'SOG1'
189. 'T15F16.14'
190. 'T2I1.110'
191. 'TCP14'
192. 'TCP15'
193. 'TCP9'
194. 'TCX2'
195. 'TCX3'
196. 'TRY'
197. 'TSO1'
198. 'TTG1'
199. 'UPB1'
200. 'WER'
201. 'WIP2-2'
202. 'WLIM2B'
203. 'WOX14'
204. 'WRKY13'
205. 'WRKY44'
206. 'WRKY75'
207. 'ZFP5'
208. 'ZFP6'
209. 'ZFP8'

gsgo <- gsub(",.*$","",gsub("^.*ath:","",sann[match(gsgo, sann$X.node),]$other_names_and_aliases))
gsgo[which(gsgo=='831248')]='AT5G14000'
gsgo

1. 'AT3G53680'
2. 'AT1G71692'
3. 'AT3G57230'
4. 'AT5G35750'
5. 'AT1G27320'
6. 'AT2G01830'
7. 'AT5G57390'
8. 'AT2G02470'
9. 'AT4G00730'
10. 'AT1G79430'
11. 'AT2G28350'
12. 'AT4G30080'
13. 'AT1G77850'
14. 'AT1G19220'
15. 'AT5G20730'
16. 'AT3G16857'
17. 'AT4G31920'
18. 'AT2G25180'
19. 'AT4G16110'
20. 'AT1G69780'
21. 'AT1G52150'
22. 'AT1G26960'
23. 'AT4G32880'
24. 'AT1G70920'
25. 'AT2G31650'
26. 'AT3G50750'
27. 'AT4G00480'
28. 'AT2G31280'
29. 'AT1G63650'
30. 'AT3G25710'
31. 'AT2G42300'
32. 'AT1G27740'
33. 'AT2G24260'
34. 'AT4G30980'
35. 'AT5G58010'
36. 'AT1G66470'
37. 'AT4G33880'
38. 'AT5G37800'
39. 'AT3G45260'
40. 'AT2G46020'
41. 'AT4G35040'
42. 'AT2G16770'
43. 'AT1G19350'
44. 'AT5G14170'
45. 'AT3G06010'
46. 'AT5G19310'
47. 'AT2G46410'
48. 'AT4G23750'
49. 'AT2G39830'
50. 'AT1G07640'
51. 'AT2G28810'
52. 'AT2G37590'
53. 'AT3G45610'
54. 'AT3G55370'
55. 'AT5G02460'
56. 'AT5G60200'
57. 'AT5G62940'
58. 'AT5G16780'
59. 'AT5G03415'
60. 'AT5G22220'
61. 'AT2G03500'
62. 'AT1G36060'
63. 'AT5G61600'
64. 'AT5G56780'
65. 'AT1G01380'
66. 'AT1G66340'
67. 'AT2G36720'
68. 'AT1G62310'
69. 'AT2G23780'
70. 'AT5G11340'
71. 'AT1G26870'
72. 'AT1G14920'
73. 'AT4G21060'
74. 'AT5G25830'
75. 'AT5G26930'
76. 'AT5G28640'
77. 'AT1G68360'
78. 'AT1G79840'
79. 'AT5G41315'
80. 'AT4G37740'
81. 'AT2G36400'
82. 'AT1G33240'
83. 'AT3G54610'
84. 'AT5G47370'
85. 'AT4G16780'
86. 'AT1G73360'
87. 'AT1G17920'
88. 'AT1G05230'
89. 'AT3G44750'
90. 'AT3G25790'
91. 'AT1G68670'
92. 'AT1G25550'
93. 'AT1G49560'
94. 'AT1G13300'
95. 'AT5G45710'
96. 'AT1G04550'
97. 'AT4G14550'
98. 'AT2G46990'
99. 'AT5G25890'
100. 'AT1G04240'
101. 'AT3G17600'
102. 'AT2G22670'
103. 'AT2G02080'
104. 'AT1G14580'
105. 'AT1G32130'
106. 'AT5G03150'
107. 'AT4G20400'
108. 'AT3G07610'
109. 'AT3G20810'
110. 'AT3G45880'
111. 'AT5G38840'
112. 'AT5G16560'
113. 'AT1G62990'
114. 'AT5G47390'
115. 'AT2G40470'
116. 'AT2G42430'
117. 'AT2G45420'
118. 'AT3G58190'
119. 'AT1G16530'
120. 'AT4G00220'
121. 'AT1G31320'
122. 'AT1G62830'
123. 'AT5G13910'
124. 'AT2G27230'
125. 'AT5G12330'
126. 'AT5G45420'
127. 'AT1G03840'
128. 'AT1G74660'
129. 'AT5G55580'
130. 'AT2G47460'
131. 'AT1G14350'
132. 'AT1G66230'
133. 'AT5G40330'
134. 'AT4G34990'
135. 'AT5G57620'
136. 'AT5G16600'
137. 'AT5G12870'
138. 'AT1G17950'
139. 'AT1G08810'
140. 'AT1G09540'
141. 'AT2G16720'
142. 'AT3G08500'
143. 'AT4G22680'
144. 'AT5G26660'
145. 'AT2G02820'
146. 'AT5G46760'
147. 'AT4G17880'
148. 'AT5G13780'
149. 'AT1G02220'
150. 'AT1G02250'
151. 'AT1G12260'
152. 'AT1G28470'
153. 'AT1G62700'
154. 'AT1G71930'
155. 'AT2G18060'
156. 'AT2G46770'
157. 'AT3G03200'
158. 'AT3G15510'
159. 'AT4G29230'
160. 'AT4G36160'
161. 'AT5G13180'
162. 'AT5G14000'
163. 'AT5G17260'
164. 'AT5G62380'
165. 'AT5G64530'
166. 'AT5G66300'
167. 'AT5G39610'
168. 'AT5G44160'
169. 'AT2G25170'
170. 'AT3G20840'
171. 'AT1G51190'
172. 'AT1G74500'
173. 'AT2G12646'
174. 'AT5G19790'
175. 'AT1G53910'
176. 'AT1G13260'
177. 'AT3G12280'
178. 'AT3G48430'
179. 'AT5G60690'
180. 'AT2G01570'
181. 'AT3G60630'
182. 'AT2G45160'
183. 'AT4G00150'
184. 'AT3G54220'
185. 'AT3G26744'
186. 'AT4G37650'
187. 'AT1G79580'
188. 'AT1G25580'
189. 'AT4G08455'
190. 'AT5G07400'
191. 'AT3G47620'
192. 'AT1G69690'
193. 'AT2G45680'
194. 'AT4G14770'
195. 'AT3G22760'
196. 'AT5G53200'
197. 'AT3G22780'
198. 'AT5G24520'
199. 'AT2G47270'
200. 'AT5G14750'
201. 'AT3G57670'
202. 'AT3G55770'
203. 'AT1G20700'
204. 'AT4G39410'
205. 'AT2G37260'
206. 'AT5G13080'
207. 'AT1G10480'
208. 'AT1G67030'
209. 'AT2G41940'

sann[which(sann$X.node=="HAT7"),]

A data.frame: 1 x 5

	X.node	identifier	domain_summary_url	annotation	other_names_and_aliases
	<chr>	<chr>	<chr>	<chr>	<chr>
668	HAT7	3702.Q00466	Homeobox-leucine zipper protein HAT7; Probable transcription factor.	https://smart.embl.de/smart/DDvec.cgi?smart=314:HOX(113|174)+	831367,AT5G15150,ATHB-3,At5g15150,F8M21_40,HAT7,HAT7_ARATH,HB-3,HD-ZIP protein 7,HD-ZIP protein ATHB-3,Homeobox 3,Homeobox-leucine zipper protein,Homeobox-leucine zipper protein HAT7,Homeodomain transcription factor ATHB-3,Homeodomain-leucine zipper protein HAT7,NM_121519.3,NP_568309,NP_568309.2,Q00466,Q0WNS2,Q9LXG6,ath:AT5G15150

sann[grep("831248",sann$other_names_and_aliases, ignore.case = TRUE),]

A data.frame: 1 x 5

	X.node	identifier	domain_summary_url	annotation	other_names_and_aliases
	<chr>	<chr>	<chr>	<chr>	<chr>
980	NAC084	3702.A0A1P8BAC2	NAC domain containing protein 84.	https://smart.embl.de/smart/DDvec.cgi?smart=262:Pfam_NAM(16|196)+	831248,A0A1P8BAC2,A0A1P8BAC2_ARATH,At5g14000,MAC12.3,MAC12_3,NAC domain containing protein 84,NAC084,NM_001343305.1,NP_001330278.1,anac084

R50

r50 <- 105
numz <- function(x){
    sum(x==0)/length(x)
}

GeneSys

genesys <- dat

run_r50_genesys <- function(x){
    genesys$combined_score <- min_max_normalize(rowSums(apply(genesys[,grep(x,colnames(genesys))],2,as.numeric)))
    genesys <- genesys %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(genesys))){
        if (genesys$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## all cell type : in centrality + celltype & dev stage specificity
#dat$combined_score <- min_max_normalize(rowSums(apply(dat[,grep("in_centrality",colnames(dat))],2,as.numeric)))
#dat$celltype_specificity <- min_max_normalize(apply(apply(dat[,grep("in_centrality",colnames(dat))],2,as.numeric),1,numz))
#dat$weighted_score <- dat$combined_score + dat$celltype_specificity
#dat <- dat %>% arrange(desc(weighted_score))

#count <- 0
#for (i in seq(nrow(dat))){
#    if (dat$GeneID[i] %in% gsgo){
#        count <- count +1
#        if (count == r50){
#            print(i)
#            break
#        }
#    }
#}

CellOracle

prepros <- function(x){
    dat <- read.csv(x)
    #dat <- dat %>% filter(role=="Connector Hub") 
    dat <- dat[,c(1, grep("centrality",colnames(dat)), 22)]
    return(dat)
}

atri <- prepros("../celloracle/atrichoblast_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
tri <- prepros("../celloracle/trichoblast_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
lrc <- prepros("../celloracle/lrc_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
cor <- prepros("../celloracle/cortex_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
end <- prepros("../celloracle/endodermis_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
per <- prepros("../celloracle/pericycle_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
pro <- prepros("../celloracle/procambium_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
xyl <- prepros("../celloracle/xylem_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
phl <- prepros("../celloracle/phloem_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")
col <- prepros("../celloracle/columella_Root_Atlas_SCT_celloracle_gene_score_iGRN.csv")

dat <- rbind(atri, tri, lrc, cor, end, per, pro, xyl, phl, col)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all), 
                                  in_centrality = min_max_normalize(degree_centrality_in), 
                                  out_centrality = min_max_normalize(degree_centrality_out),
                                 betweenness_centrality = min_max_normalize(betweenness_centrality),
                                 closeness_centrality = min_max_normalize(closeness_centrality),
                                 eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality), 
                                  in_centrality = sum(in_centrality), 
                                  out_centrality = sum(out_centrality),
                                 betweenness_centrality = sum(betweenness_centrality),
                                 closeness_centrality = sum(closeness_centrality),
                                 eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:7],2,min_max_normalize))

head(dat)

A data.frame: 6 x 7

	GeneID	degree_centrality	in_centrality	out_centrality	betweenness_centrality	closeness_centrality	eigenvector_centrality
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	AT1G01010	0.0849393998	0.065317964	0.081735954	2.592974e-02	0.3643692412	0.068339145
2	AT1G01030	0.0009542021	0.000000000	0.004447568	0.000000e+00	0.0003449773	0.003632920
3	AT1G01260	0.0233438132	0.000000000	0.033565069	0.000000e+00	0.0872081085	0.016136854
4	AT1G01350	0.0293468347	0.134378356	0.000000000	0.000000e+00	0.0000000000	0.017764785
5	AT1G01380	0.0035614661	0.005492245	0.004573149	7.240465e-05	0.0852853904	0.003752290
6	AT1G01640	0.0060441842	0.023215800	0.000000000	0.000000e+00	0.0000000000	0.006672674

celloracle <- dat

run_r50_celloracle <- function(x){
    celloracle$combined_score <- celloracle[,grep(x,colnames(celloracle))]
    celloracle <- celloracle %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(celloracle))){
        if (celloracle$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## all cell type : degree centrality + out centrality + in centrality + betweenness centrality + closeness + eigenvector
#dat$combined_score <- min_max_normalize(rowSums(apply(dat[,grep("centrality",colnames(dat))],2,as.numeric)))
#dat <- dat %>% arrange(desc(combined_score))

#count <- 0
#for (i in seq(nrow(dat))){
#    if (dat$GeneID[i] %in% gsgo){
#        count <- count +1
#        if (count == r50){
#            print(i)
#            break
#        }
#    }
#}

#dat$combined_score <- min_max_normalize(rowSums(apply(dat[,grep("betweenness_centrality|celltype_specificity",colnames(dat))],2,as.numeric)))
#dat <- dat %>% arrange(desc(combined_score))

#count <- 0
#for (i in seq(nrow(dat))){
#    if (dat$GeneID[i] %in% gsgo){
#        count <- count +1
#        if (count == r50){
#            print(i)
#            break
#        }
#    }
#}

DE (Root_Atlas)

de <- read.csv("Root_Atlas_DE_Gene_List.csv")

de <- de %>% arrange(pct.diff_rank) %>% arrange(avg_diff_rank)%>% arrange(myAUC_rank)%>% arrange(combined_rank)

## Remove those not present in the dataset
de <- de[!is.na(match(de$gene.ID,exptf)),]

head(de)

A data.frame: 6 x 14

	myAUC	avg_diff	power	pct.1	pct.2	celltype	pseudotime.bin	gene.ID	gene.name	pct.diff	pct.diff_rank	avg_diff_rank	myAUC_rank	combined_rank
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>	<chr>	<chr>	<dbl>	<int>	<int>	<int>	<int>
13	0.901	1.985825	0.802	0.959	0.287	Endodermis	T4	AT5G13910	LEP	0.672	2	4	1	1
40	0.934	2.820810	0.868	0.900	0.155	Procambium	T0	AT1G54690	HTA3	0.745	2	35	2	1
51	0.841	2.351595	0.682	0.881	0.297	Procambium	T1	AT1G25560	TEM1	0.584	1	11	3	1
58	0.979	3.799126	0.958	1.000	0.046	Xylem	T1	AT4G22680	MYB85	0.954	1	7	4	1
63	0.945	4.666402	0.890	0.765	0.054	Phloem	T2	AT3G60530	GATA4	0.711	35	1	5	1
66	0.871	2.623028	0.742	0.877	0.242	Procambium	T2	AT1G66600	WRKY63	0.635	1	5	5	1

dat <- de %>% group_by(gene.ID) %>% reframe(combined_rank = mean(combined_rank),myAUC_rank = mean(myAUC_rank),pct.diff_rank = mean(pct.diff_rank),avg_diff_rank = mean(avg_diff_rank))
dat <- dat %>% arrange(combined_rank)
colnames(dat) <- c("GeneID","combined_rank","myAUC_rank","pct.diff_rank","avg_diff_rank")

head(dat)

A tibble: 6 x 5

GeneID	combined_rank	myAUC_rank	pct.diff_rank	avg_diff_rank
<chr>	<dbl>	<dbl>	<dbl>	<dbl>
AT1G66600	6.500000	19.50000	23.00000	11.50000
AT5G58010	8.333333	9.00000	7.00000	16.00000
AT1G13600	9.500000	42.00000	34.50000	19.50000
AT4G37260	12.333333	10.33333	20.33333	41.66667
AT1G26680	13.000000	73.00000	37.00000	78.00000
AT1G61660	13.000000	19.66667	17.33333	38.33333

de <- dat

run_r50_de <- function(x){
    de$combined_score <- de[,grep(x,colnames(de))]
    de <- de %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(de))){
        if (de$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

Plot r50 with ranked by different centrality

## Expressed TFs permutation
R50_permutation <- c()
for (j in 1:1000){
ran <- sample(exptf, length(exptf), replace=FALSE)
count <- 0
for (i in seq(length(ran))){
    if (ran[i] %in% gsgo){
        count <- count +1
        if (count == r50){
            R50_permutation <- c(R50_permutation,i)
            break
        }
    }
}
}

toplt <- data.frame(Methods=c("GeneSys degree centrality", "GeneSys out centrality", "GeneSys in centrality", "GeneSys betweenness centrality", 
                 "GeneSys closeness centrality", "GeneSys eigenvector centrality","CellOracle degree centrality", "CellOracle out centrality", 
                 "CellOracle in centrality", "CellOracle betweenness centrality", "CellOracle closeness centrality", "CellOracle eigenvector centrality",
                 "DE myAUC rank", "DE pct diff rank", "DE avg diff rank", "Expressed TFs permutation"),
           R50=c(run_r50_genesys('degree_centrality'),run_r50_genesys('out_centrality'),run_r50_genesys('in_centrality'),
                 run_r50_genesys('betweenness_centrality'),run_r50_genesys('closeness_centrality'),run_r50_genesys('eigenvector_centrality'),
                 run_r50_celloracle('degree_centrality'),run_r50_celloracle('out_centrality'),run_r50_celloracle('in_centrality'),
                 run_r50_celloracle('betweenness_centrality'),run_r50_celloracle('closeness_centrality'),run_r50_celloracle('eigenvector_centrality'),
                 run_r50_de('myAUC_rank'),run_r50_de('pct.diff_rank'),run_r50_de('avg_diff_rank'), mean(R50_permutation)))

toplt

A data.frame: 16 x 2

Methods	R50
<chr>	<dbl>
GeneSys degree centrality	403.000
GeneSys out centrality	409.000
GeneSys in centrality	383.000
GeneSys betweenness centrality	372.000
GeneSys closeness centrality	695.000
GeneSys eigenvector centrality	460.000
CellOracle degree centrality	479.000
CellOracle out centrality	469.000
CellOracle in centrality	461.000
CellOracle betweenness centrality	413.000
CellOracle closeness centrality	505.000
CellOracle eigenvector centrality	488.000
DE myAUC rank	484.000
DE pct diff rank	495.000
DE avg diff rank	472.000
Expressed TFs permutation	749.023

options(repr.plot.width=8, repr.plot.height=8)
ggplot(toplt, aes(x=reorder(Methods, R50, decreasing = TRUE),  y=R50)) + geom_point(size=4)+ 
  labs(title="TF Prioritization Performance (R50)",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

toplt <- data.frame(Methods=factor(c(rep("GeneSys",6), rep("CellOracle",6), rep("DE",3), "Expressed TFs permutation"), 
                                   levels=c("Expressed TFs permutation", "DE", "CellOracle", "GeneSys")), R50=toplt$R50)

options(repr.plot.width=9.5, repr.plot.height=6)
ggplot(toplt, aes(x=Methods, y=R50))+
  geom_boxplot(width=0.3)+ 
  labs(title="TF Prioritization Performance (R50)",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

R50 for Tissue specific TFs

Stele (xylem, phloem, procambium)

gsgo <- read.csv("./Gold_Standard_Root_TF_StringDB.csv", header=TRUE)
gsgo <- gsgo[which(gsgo[,5]=="Yes"),]
gsgo$preferredName
gsgo <- gsgo$GeneID

1. 'APL'
2. 'ATHB-8'
3. 'BHLH48'
4. 'DAR2'
5. 'DOF5.3'
6. 'DOF5.6'
7. 'DOT2'
8. 'ERF104'
9. 'ET2'
10. 'GATA12'
11. 'KAN1'
12. 'KNAT7'
13. 'LBD15'
14. 'LBD18'
15. 'LBD30'
16. 'LBD4'
17. 'LHW'
18. 'MYB20'
19. 'MYB32'
20. 'MYB43'
21. 'MYB46'
22. 'MYB52'
23. 'MYB61'
24. 'MYB7'
25. 'MYB83'
26. 'MYB85'
27. 'NAC005'
28. 'NAC007'
29. 'NAC010'
30. 'NAC026'
31. 'NAC030'
32. 'NAC037'
33. 'NAC043'
34. 'NAC045'
35. 'NAC075'
36. 'NAC076'
37. 'NAC083'
38. 'NAC086'
39. 'NAC101'
40. 'NAC104'
41. 'NAC105'
42. 'REV'
43. 'WOX14'

length(gsgo)

43

r50 <- 22

## Define genesys
run_r50_genesys <- function(x){
    genesys$ct_score <- min_max_normalize(rowSums(apply(genesys[,grep(paste0('xyl_',x,'|phl_',x,'|pro_',x),colnames(genesys))],2,as.numeric)))
    genesys$combined_score <- genesys$ct_score
    
    genesys <- genesys %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(genesys))){
        if (genesys$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define celloracle
dat <- rbind(pro, xyl, phl)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all), 
                                  in_centrality = min_max_normalize(degree_centrality_in), 
                                  out_centrality = min_max_normalize(degree_centrality_out),
                                 betweenness_centrality = min_max_normalize(betweenness_centrality),
                                 closeness_centrality = min_max_normalize(closeness_centrality),
                                 eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality), 
                                  in_centrality = sum(in_centrality), 
                                  out_centrality = sum(out_centrality),
                                 betweenness_centrality = sum(betweenness_centrality),
                                 closeness_centrality = sum(closeness_centrality),
                                 eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:7],2,min_max_normalize))

celloracle <- dat

run_r50_celloracle <- function(x){
    celloracle$combined_score <- celloracle[,grep(x,colnames(celloracle))]
    celloracle <- celloracle %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(celloracle))){
        if (celloracle$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define DE
de <- read.csv("Root_Atlas_DE_Gene_List.csv")
de <- de %>% filter(celltype=="Xylem"|celltype=="Phloem"|celltype=="Procambium")
## Remove those not present in the dataset
de <- de[!is.na(match(de$gene.ID,exptf)),]
dat <- de %>% group_by(gene.ID) %>% reframe(combined_rank = mean(combined_rank),myAUC_rank = mean(myAUC_rank),pct.diff_rank = mean(pct.diff_rank),avg_diff_rank = mean(avg_diff_rank))
dat <- dat %>% arrange(combined_rank)
colnames(dat) <- c("GeneID","combined_rank","myAUC_rank","pct.diff_rank","avg_diff_rank")
de <- dat
run_r50_de <- function(x){
    de$combined_score <- de[,grep(x,colnames(de))]
    de <- de %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(de))){
        if (de$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Expressed TFs permutation
R50_permutation <- c()
for (j in 1:1000){
ran <- sample(exptf, length(exptf), replace=FALSE)
count <- 0
for (i in seq(length(ran))){
    if (ran[i] %in% gsgo){
        count <- count +1
        if (count == r50){
            R50_permutation <- c(R50_permutation,i)
            break
        }
    }
}
}

toplt <- data.frame(Methods=c("GeneSys degree centrality", "GeneSys out centrality", "GeneSys in centrality", "GeneSys betweenness centrality", 
                 "GeneSys closeness centrality", "GeneSys eigenvector centrality","CellOracle degree centrality", "CellOracle out centrality", 
                 "CellOracle in centrality", "CellOracle betweenness centrality", "CellOracle closeness centrality", "CellOracle eigenvector centrality",
                 "DE myAUC rank", "DE pct diff rank", "DE avg diff rank", "Expressed TFs permutation"),
           R50=c(run_r50_genesys('degree_centrality'),run_r50_genesys('out_centrality'),run_r50_genesys('in_centrality'),
                 run_r50_genesys('betweenness_centrality'),run_r50_genesys('closeness_centrality'),run_r50_genesys('eigenvector_centrality'),
                 run_r50_celloracle('degree_centrality'),run_r50_celloracle('out_centrality'),run_r50_celloracle('in_centrality'),
                 run_r50_celloracle('betweenness_centrality'),run_r50_celloracle('closeness_centrality'),run_r50_celloracle('eigenvector_centrality'),
                 run_r50_de('myAUC_rank'),run_r50_de('pct.diff_rank'),run_r50_de('avg_diff_rank'), mean(R50_permutation)))

toplt

A data.frame: 16 x 2

Methods	R50
<chr>	<dbl>
GeneSys degree centrality	94.000
GeneSys out centrality	159.000
GeneSys in centrality	91.000
GeneSys betweenness centrality	137.000
GeneSys closeness centrality	474.000
GeneSys eigenvector centrality	160.000
CellOracle degree centrality	288.000
CellOracle out centrality	255.000
CellOracle in centrality	190.000
CellOracle betweenness centrality	154.000
CellOracle closeness centrality	337.000
CellOracle eigenvector centrality	252.000
DE myAUC rank	275.000
DE pct diff rank	302.000
DE avg diff rank	245.000
Expressed TFs permutation	747.206

options(repr.plot.width=12, repr.plot.height=8)
ggplot(toplt, aes(x=reorder(Methods, R50, decreasing = TRUE),  y=R50)) + geom_point(size=4)+ 
  labs(title="Stele-specific TF Prioritization Performance (R50)",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

toplt <- data.frame(Methods=factor(c(rep("GeneSys",6), rep("CellOracle",6), rep("DE",3), "Expressed TFs permutation"), 
                                   levels=c("Expressed TFs permutation", "DE", "CellOracle", "GeneSys")), R50=toplt$R50)

options(repr.plot.width=9.5, repr.plot.height=6)
ggplot(toplt, aes(x=Methods, y=R50))+
  geom_boxplot(width=0.3)+ 
  labs(title="Stele-specific TF Prioritization Performance",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

Epidermis

gsgo <- read.csv("./Gold_Standard_Root_TF_StringDB.csv", header=TRUE)
gsgo <- gsgo[which(gsgo[,6]=="Yes"),]
gsgo$preferredName
gsgo <- gsgo$GeneID

1. 'AGL16'
2. 'AL6'
3. 'ANL2'
4. 'BHLH12'
5. 'BHLH2'
6. 'BHLH32'
7. 'BHLH54'
8. 'BHLH66'
9. 'BHLH69'
10. 'BHLH82'
11. 'BHLH83'
12. 'BHLH85'
13. 'BHLH86'
14. 'CPC'
15. 'E2FB'
16. 'ETC1'
17. 'GALT2'
18. 'GIS3'
19. 'GL2'
20. 'GL3'
21. 'GTL1'
22. 'HDG11'
23. 'HDG12'
24. 'HDG2'
25. 'JMJ25'
26. 'MAMYB'
27. 'MYB124'
28. 'MYB23'
29. 'MYB86'
30. 'MYB88'
31. 'MYC3'
32. 'MYC4'
33. 'RBR1'
34. 'SCL22'
35. 'SCL27'
36. 'SCL6'
37. 'SCRM'
38. 'TCX2'
39. 'TCX3'
40. 'TRY'
41. 'TSO1'
42. 'TTG1'
43. 'WER'
44. 'WRKY44'
45. 'WRKY75'
46. 'ZFP5'
47. 'ZFP6'
48. 'ZFP8'

length(gsgo)
r50 <- 24

48

## Define genesys
run_r50_genesys <- function(x){
    genesys$ct_score <- min_max_normalize(rowSums(apply(genesys[,grep(paste0('atri_',x,'|tri_',x,'|lrc_',x),colnames(genesys))],2,as.numeric)))
    genesys$combined_score <- genesys$ct_score
    
    genesys <- genesys %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(genesys))){
        if (genesys$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define celloracle
dat <- rbind(atri, tri, lrc)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all), 
                                  in_centrality = min_max_normalize(degree_centrality_in), 
                                  out_centrality = min_max_normalize(degree_centrality_out),
                                 betweenness_centrality = min_max_normalize(betweenness_centrality),
                                 closeness_centrality = min_max_normalize(closeness_centrality),
                                 eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality), 
                                  in_centrality = sum(in_centrality), 
                                  out_centrality = sum(out_centrality),
                                 betweenness_centrality = sum(betweenness_centrality),
                                 closeness_centrality = sum(closeness_centrality),
                                 eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:7],2,min_max_normalize))

celloracle <- dat

run_r50_celloracle <- function(x){
    celloracle$combined_score <- celloracle[,grep(x,colnames(celloracle))]
    celloracle <- celloracle %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(celloracle))){
        if (celloracle$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define DE
de <- read.csv("Root_Atlas_DE_Gene_List.csv")
de <- de %>% filter(celltype=="Atrichoblast"|celltype=="Trichoblast"|celltype=="Lateral Root Cap")
## Remove those not present in the dataset
de <- de[!is.na(match(de$gene.ID,exptf)),]
dat <- de %>% group_by(gene.ID) %>% reframe(combined_rank = mean(combined_rank),myAUC_rank = mean(myAUC_rank),pct.diff_rank = mean(pct.diff_rank),avg_diff_rank = mean(avg_diff_rank))
dat <- dat %>% arrange(combined_rank)
colnames(dat) <- c("GeneID","combined_rank","myAUC_rank","pct.diff_rank","avg_diff_rank")
de <- dat
run_r50_de <- function(x){
    de$combined_score <- de[,grep(x,colnames(de))]
    de <- de %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(de))){
        if (de$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Expressed TFs permutation
R50_permutation <- c()
for (j in 1:1000){
ran <- sample(exptf, length(exptf), replace=FALSE)
count <- 0
for (i in seq(length(ran))){
    if (ran[i] %in% gsgo){
        count <- count +1
        if (count == r50){
            R50_permutation <- c(R50_permutation,i)
            break
        }
    }
}
}

toplt <- data.frame(Methods=c("GeneSys degree centrality", "GeneSys out centrality", "GeneSys in centrality", "GeneSys betweenness centrality", 
                 "GeneSys closeness centrality", "GeneSys eigenvector centrality","CellOracle degree centrality", "CellOracle out centrality", 
                 "CellOracle in centrality", "CellOracle betweenness centrality", "CellOracle closeness centrality", "CellOracle eigenvector centrality",
                 "DE myAUC rank", "DE pct diff rank", "DE avg diff rank", "Expressed TFs permutation"),
           R50=c(run_r50_genesys('degree_centrality'),run_r50_genesys('out_centrality'),run_r50_genesys('in_centrality'),
                 run_r50_genesys('betweenness_centrality'),run_r50_genesys('closeness_centrality'),run_r50_genesys('eigenvector_centrality'),
                 run_r50_celloracle('degree_centrality'),run_r50_celloracle('out_centrality'),run_r50_celloracle('in_centrality'),
                 run_r50_celloracle('betweenness_centrality'),run_r50_celloracle('closeness_centrality'),run_r50_celloracle('eigenvector_centrality'),
                 999,999,999, mean(R50_permutation)))

toplt

A data.frame: 16 x 2

Methods	R50
<chr>	<dbl>
GeneSys degree centrality	288.00
GeneSys out centrality	339.00
GeneSys in centrality	318.00
GeneSys betweenness centrality	329.00
GeneSys closeness centrality	355.00
GeneSys eigenvector centrality	309.00
CellOracle degree centrality	391.00
CellOracle out centrality	365.00
CellOracle in centrality	455.00
CellOracle betweenness centrality	304.00
CellOracle closeness centrality	351.00
CellOracle eigenvector centrality	392.00
DE myAUC rank	999.00
DE pct diff rank	999.00
DE avg diff rank	999.00
Expressed TFs permutation	729.58

options(repr.plot.width=12, repr.plot.height=8)
ggplot(toplt, aes(x=reorder(Methods, R50, decreasing = TRUE),  y=R50)) + geom_point(size=4)+ 
  labs(title="Epidermis-specific TF Prioritization Performance (R50)",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

toplt <- data.frame(Methods=factor(c(rep("GeneSys",6), rep("CellOracle",6), rep("DE",3), "Expressed TFs permutation"), 
                                   levels=c("DE","Expressed TFs permutation", "CellOracle", "GeneSys")), R50=toplt$R50)

options(repr.plot.width=9.5, repr.plot.height=6)
ggplot(toplt, aes(x=Methods, y=R50))+
  geom_boxplot(width=0.3)+ 
  labs(title="Epidermis-specific TF Prioritization Performance",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

R50 for celltype specific

Xylem

gsgo <- read.csv("./Gold_Standard_Root_TF_StringDB.csv", header=TRUE)
gsgo <- gsgo[which(gsgo[,7]=="Yes"),]
gsgo$preferredName
gsgo <- gsgo$GeneID

1. 'APL'
2. 'ATHB-8'
3. 'BHLH48'
4. 'ET2'
5. 'KNAT7'
6. 'LBD18'
7. 'LBD30'
8. 'LHW'
9. 'MYB20'
10. 'MYB32'
11. 'MYB43'
12. 'MYB46'
13. 'MYB52'
14. 'MYB61'
15. 'MYB7'
16. 'MYB83'
17. 'MYB85'
18. 'NAC005'
19. 'NAC007'
20. 'NAC010'
21. 'NAC026'
22. 'NAC030'
23. 'NAC037'
24. 'NAC043'
25. 'NAC076'
26. 'NAC083'
27. 'NAC101'
28. 'NAC104'
29. 'NAC105'
30. 'REV'

length(gsgo)
r50 <- 15

30

## Define genesys
run_r50_genesys <- function(x){
    genesys$ct_score <- min_max_normalize(rowSums(apply(genesys[,grep(paste0('xyl_',x),colnames(genesys))],2,as.numeric)))
    genesys$combined_score <- genesys$ct_score
    
    genesys <- genesys %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(genesys))){
        if (genesys$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define celloracle
dat <- rbind(xyl)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all), 
                                  in_centrality = min_max_normalize(degree_centrality_in), 
                                  out_centrality = min_max_normalize(degree_centrality_out),
                                 betweenness_centrality = min_max_normalize(betweenness_centrality),
                                 closeness_centrality = min_max_normalize(closeness_centrality),
                                 eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality), 
                                  in_centrality = sum(in_centrality), 
                                  out_centrality = sum(out_centrality),
                                 betweenness_centrality = sum(betweenness_centrality),
                                 closeness_centrality = sum(closeness_centrality),
                                 eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:7],2,min_max_normalize))

celloracle <- dat

run_r50_celloracle <- function(x){
    celloracle$combined_score <- celloracle[,grep(x,colnames(celloracle))]
    celloracle <- celloracle %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(celloracle))){
        if (celloracle$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define DE
de <- read.csv("Root_Atlas_DE_Gene_List.csv")
de <- de %>% filter(celltype=="Xylem")
## Remove those not present in the dataset
de <- de[!is.na(match(de$gene.ID,exptf)),]
dat <- de %>% group_by(gene.ID) %>% reframe(combined_rank = mean(combined_rank),myAUC_rank = mean(myAUC_rank),pct.diff_rank = mean(pct.diff_rank),avg_diff_rank = mean(avg_diff_rank))
dat <- dat %>% arrange(combined_rank)
colnames(dat) <- c("GeneID","combined_rank","myAUC_rank","pct.diff_rank","avg_diff_rank")
de <- dat
run_r50_de <- function(x){
    de$combined_score <- de[,grep(x,colnames(de))]
    de <- de %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(de))){
        if (de$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Expressed TFs permutation
R50_permutation <- c()
for (j in 1:1000){
ran <- sample(exptf, length(exptf), replace=FALSE)
count <- 0
for (i in seq(length(ran))){
    if (ran[i] %in% gsgo){
        count <- count +1
        if (count == r50){
            R50_permutation <- c(R50_permutation,i)
            break
        }
    }
}
}

toplt <- data.frame(Methods=c("GeneSys degree centrality", "GeneSys out centrality", "GeneSys in centrality", "GeneSys betweenness centrality", 
                 "GeneSys closeness centrality", "GeneSys eigenvector centrality","CellOracle degree centrality", "CellOracle out centrality", 
                 "CellOracle in centrality", "CellOracle betweenness centrality", "CellOracle closeness centrality", "CellOracle eigenvector centrality",
                 "DE myAUC rank", "DE pct diff rank", "DE avg diff rank", "Expressed TFs permutation"),
           R50=c(run_r50_genesys('degree_centrality'),run_r50_genesys('out_centrality'),run_r50_genesys('in_centrality'),
                 run_r50_genesys('betweenness_centrality'),run_r50_genesys('closeness_centrality'),run_r50_genesys('eigenvector_centrality'),
                 run_r50_celloracle('degree_centrality'),run_r50_celloracle('out_centrality'),run_r50_celloracle('in_centrality'),
                 run_r50_celloracle('betweenness_centrality'),run_r50_celloracle('closeness_centrality'),run_r50_celloracle('eigenvector_centrality'),
                 run_r50_de('myAUC_rank'),run_r50_de('pct.diff_rank'),run_r50_de('avg_diff_rank'), mean(R50_permutation)))

toplt

A data.frame: 16 x 2

Methods	R50
<chr>	<dbl>
GeneSys degree centrality	30.000
GeneSys out centrality	65.000
GeneSys in centrality	27.000
GeneSys betweenness centrality	54.000
GeneSys closeness centrality	47.000
GeneSys eigenvector centrality	37.000
CellOracle degree centrality	90.000
CellOracle out centrality	88.000
CellOracle in centrality	65.000
CellOracle betweenness centrality	50.000
CellOracle closeness centrality	135.000
CellOracle eigenvector centrality	62.000
DE myAUC rank	130.000
DE pct diff rank	133.000
DE avg diff rank	108.000
Expressed TFs permutation	725.408

options(repr.plot.width=12, repr.plot.height=8)
ggplot(toplt, aes(x=reorder(Methods, R50, decreasing = TRUE),  y=R50)) + geom_point(size=4)+ 
  labs(title="Xylem-specific TF Prioritization Performance (R50)",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

toplt <- data.frame(Methods=factor(c(rep("GeneSys",6), rep("CellOracle",6), rep("DE",3), "Expressed TFs permutation"), 
                                   levels=c("Expressed TFs permutation","DE", "CellOracle", "GeneSys")), R50=toplt$R50)

options(repr.plot.width=9.5, repr.plot.height=6)
ggplot(toplt, aes(x=Methods, y=R50))+
  geom_boxplot(width=0.3)+ 
  labs(title="Xylem-specific TF Prioritization Performance",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

Trichoblast

gsgo <- read.csv("./Gold_Standard_Root_TF_StringDB.csv", header=TRUE)
gsgo <- gsgo[which(gsgo[,8]=="Yes"),]
gsgo$preferredName
gsgo <- gsgo$GeneID

1. 'AL6'
2. 'ANL2'
3. 'BHLH12'
4. 'BHLH2'
5. 'BHLH32'
6. 'BHLH54'
7. 'BHLH66'
8. 'BHLH69'
9. 'BHLH82'
10. 'BHLH83'
11. 'BHLH85'
12. 'BHLH86'
13. 'CPC'
14. 'E2FB'
15. 'ETC1'
16. 'GALT2'
17. 'GIS3'
18. 'GL2'
19. 'GL3'
20. 'GTL1'
21. 'HDG11'
22. 'HDG12'
23. 'HDG2'
24. 'MAMYB'
25. 'MYB23'
26. 'MYB86'
27. 'RBR1'
28. 'SCL22'
29. 'SCL27'
30. 'SCL6'
31. 'TRY'
32. 'TTG1'
33. 'WER'
34. 'ZFP5'
35. 'ZFP6'
36. 'ZFP8'

length(gsgo)
r50 <- 18

36

## Define genesys
run_r50_genesys <- function(x){
    genesys$ct_score <- min_max_normalize(rowSums(apply(genesys[,grep(paste0('^tri_',x),colnames(genesys))],2,as.numeric)))
    genesys$combined_score <- genesys$ct_score
    
    genesys <- genesys %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(genesys))){
        if (genesys$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define celloracle
dat <- rbind(tri)
## Remove those not present in the dataset
dat <- dat[!is.na(match(dat$X,exptf)),]
dat <- dat %>% group_by(cluster) %>% reframe(GeneID=X,degree_centrality = min_max_normalize(degree_centrality_all), 
                                  in_centrality = min_max_normalize(degree_centrality_in), 
                                  out_centrality = min_max_normalize(degree_centrality_out),
                                 betweenness_centrality = min_max_normalize(betweenness_centrality),
                                 closeness_centrality = min_max_normalize(closeness_centrality),
                                 eigenvector_centrality = min_max_normalize(eigenvector_centrality))
dat <- dat %>% group_by(GeneID) %>% reframe(degree_centrality = sum(degree_centrality), 
                                  in_centrality = sum(in_centrality), 
                                  out_centrality = sum(out_centrality),
                                 betweenness_centrality = sum(betweenness_centrality),
                                 closeness_centrality = sum(closeness_centrality),
                                 eigenvector_centrality = sum(eigenvector_centrality))
dat <- cbind(dat[,1],apply(dat[,2:7],2,min_max_normalize))

celloracle <- dat

run_r50_celloracle <- function(x){
    celloracle$combined_score <- celloracle[,grep(x,colnames(celloracle))]
    celloracle <- celloracle %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(celloracle))){
        if (celloracle$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Define DE
de <- read.csv("Root_Atlas_DE_Gene_List.csv")
de <- de %>% filter(celltype=="Trichoblast")
## Remove those not present in the dataset
de <- de[!is.na(match(de$gene.ID,exptf)),]
dat <- de %>% group_by(gene.ID) %>% reframe(combined_rank = mean(combined_rank),myAUC_rank = mean(myAUC_rank),pct.diff_rank = mean(pct.diff_rank),avg_diff_rank = mean(avg_diff_rank))
dat <- dat %>% arrange(combined_rank)
colnames(dat) <- c("GeneID","combined_rank","myAUC_rank","pct.diff_rank","avg_diff_rank")
de <- dat
run_r50_de <- function(x){
    de$combined_score <- de[,grep(x,colnames(de))]
    de <- de %>% arrange(desc(combined_score))
    count <- 0
    for (i in seq(nrow(de))){
        if (de$GeneID[i] %in% gsgo){
            count <- count +1
            if (count == r50){
                return(i)
                break
            }
        }
    }
}

## Expressed TFs permutation
R50_permutation <- c()
for (j in 1:1000){
ran <- sample(exptf, length(exptf), replace=FALSE)
count <- 0
for (i in seq(length(ran))){
    if (ran[i] %in% gsgo){
        count <- count +1
        if (count == r50){
            R50_permutation <- c(R50_permutation,i)
            break
        }
    }
}
}

toplt <- data.frame(Methods=c("GeneSys degree centrality", "GeneSys out centrality", "GeneSys in centrality", "GeneSys betweenness centrality", 
                 "GeneSys closeness centrality", "GeneSys eigenvector centrality","CellOracle degree centrality", "CellOracle out centrality", 
                 "CellOracle in centrality", "CellOracle betweenness centrality", "CellOracle closeness centrality", "CellOracle eigenvector centrality",
                 "DE myAUC rank", "DE pct diff rank", "DE avg diff rank", "Expressed TFs permutation"),
           R50=c(run_r50_genesys('degree_centrality'),run_r50_genesys('out_centrality'),run_r50_genesys('in_centrality'),
                 run_r50_genesys('betweenness_centrality'),run_r50_genesys('closeness_centrality'),run_r50_genesys('eigenvector_centrality'),
                 run_r50_celloracle('degree_centrality'),run_r50_celloracle('out_centrality'),run_r50_celloracle('in_centrality'),
                 run_r50_celloracle('betweenness_centrality'),run_r50_celloracle('closeness_centrality'),run_r50_celloracle('eigenvector_centrality'),
                 999,999,999, mean(R50_permutation)))

toplt

A data.frame: 16 x 2

Methods	R50
<chr>	<dbl>
GeneSys degree centrality	267.000
GeneSys out centrality	244.000
GeneSys in centrality	228.000
GeneSys betweenness centrality	325.000
GeneSys closeness centrality	398.000
GeneSys eigenvector centrality	278.000
CellOracle degree centrality	508.000
CellOracle out centrality	476.000
CellOracle in centrality	425.000
CellOracle betweenness centrality	339.000
CellOracle closeness centrality	449.000
CellOracle eigenvector centrality	501.000
DE myAUC rank	999.000
DE pct diff rank	999.000
DE avg diff rank	999.000
Expressed TFs permutation	723.912

options(repr.plot.width=12, repr.plot.height=8)
ggplot(toplt, aes(x=reorder(Methods, R50, decreasing = TRUE),  y=R50)) + geom_point(size=4)+ 
  labs(title="Trichoblast-specific TF Prioritization Performance (R50)",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

toplt <- data.frame(Methods=factor(c(rep("GeneSys",6), rep("CellOracle",6), rep("DE",3), "Expressed TFs permutation"), 
                                   levels=c("DE","Expressed TFs permutation", "CellOracle", "GeneSys")), R50=toplt$R50)

options(repr.plot.width=9.5, repr.plot.height=6)
ggplot(toplt, aes(x=Methods, y=R50))+
  geom_boxplot(width=0.3)+ 
  labs(title="Trichoblast-specific TF Prioritization Performance",x="", y = "R50")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

Plotting

dat <- genesys

plot_heatmap <- function(gene, centrality){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep(centrality,colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = TRUE, name = paste0(gene,"\n","weighted","\n","network","\n","centrality"),
            col = col_fun, column_title = paste0(str_split_i(centrality,"_",1),"\n",str_split_i(centrality,"_",2)), column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
            text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
       )
}

plot_heatmap2 <- function(gene, centrality){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep(centrality,colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
            col = col_fun, column_title = "out degree \n centrality", column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
            text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
       )
}

plot_heatmap3 <- function(gene, centrality){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep(centrality,colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1), name = paste0("unweighted","\n","network","\n","centrality"),
            col = col_fun, column_title = "in degree \n centrality", column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
            text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
       )
}

plot_all_centrality <- function(gene){
    options(repr.plot.width=10, repr.plot.height=6)
    plot_heatmap(gene,"betweenness_centrality") + plot_heatmap2(gene,"out_centrality") + plot_heatmap3(gene,"in_centrality")
}

plot_all_centrality("TTG1")

plot_all_centrality("CPC")

plot_all_centrality("SHR")

plot_all_centrality("SCR")

plot_all_centrality("BLJ")

plot_all_centrality("JKD")

plot_all_centrality("MYB36")

plot_all_centrality("RVN")

plot_all_centrality("MGP")

plot_all_centrality("NUC")

plot_all_centrality("WER")

## HAT7
plot_all_centrality("HAT7")

## GATA10
plot_all_centrality("GATA10")

## GATA11
plot_all_centrality("GATA11")

plot_all_centrality("AN3")

plot_all_centrality("GL2")

plot_all_centrality <- function(gene){
    options(repr.plot.width=8, repr.plot.height=4)
    plot_heatmap(gene,"betweenness_centrality") + plot_heatmap2(gene,"out_centrality") + plot_heatmap3(gene,"in_centrality")
}

plot_all_centrality("SHR")

plot_all_centrality("WER")

Rank by betweenness centrality, out-degree centrality, in-degree centrality and degree centrality

plot_bc <- function(gene){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep("betweenness_centrality",colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
            col = col_fun, column_title = gene, column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
            text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
       )
}

plot_oc <- function(gene){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep("out_centrality",colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
            col = col_fun, column_title = gene, column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
            text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
       )
}

plot_ic <- function(gene){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep("in_centrality",colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
            col = col_fun, column_title = gene, column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
            text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
       )
}

plot_dc <- function(gene){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep("degree_centrality",colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
            col = col_fun, column_title = gene, column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, bottom_annotation = HeatmapAnnotation(
            text = anno_text(colnames(sub), rot = 60, location = unit(1, "npc"), just = "right"))
       )
}

plot_blank <- function(gene){
    sub <- as.numeric(t(dat[which(dat$TF==gene),grep("betweenness_centrality",colnames(dat))]))
    sub <- as.matrix(cbind(sub[1:10],sub[11:20],sub[21:30],sub[31:40],sub[41:50]))
    ## Cell types
    rownames(sub) <- c('Atrichoblast','Trichoblast','LRC','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Columella')
    ## Transition
    colnames(sub) <- c('t0-t1', 't1-t3', 't3-t5', 't5-t7', 't7-t9')
    # Reorder rows
    sub <- sub[c(8,9,7,6,5,4,2,1,3,10),]
    col_fun = colorRamp2(c(0, 0.001, 1), c('white',"white", "white"))

    Heatmap(sub, rect_gp = gpar(col = "white", lwd = 1) , show_heatmap_legend = FALSE,
            col = col_fun, column_title_gp = gpar(fontsize = 12, fontface = "bold"), 
            cluster_rows = FALSE, cluster_columns = FALSE,
            show_column_names = FALSE, show_row_names = TRUE
       )
}

# Combine the top three centralies 
sub <- dat[,grep("betweenness_centrality|in_centrality|out_centrality",colnames(dat))]
sub <- as.data.frame(sapply(sub, as.numeric))
rownames(sub) <- dat$TF

head(sub)

A data.frame: 6 x 150

	atri_out_centrality_1	atri_in_centrality_1	atri_betweenness_centrality_1	tri_out_centrality_1	tri_in_centrality_1	tri_betweenness_centrality_1	lrc_out_centrality_1	lrc_in_centrality_1	lrc_betweenness_centrality_1	cor_out_centrality_1	...	pro_betweenness_centrality_5	xyl_out_centrality_5	xyl_in_centrality_5	xyl_betweenness_centrality_5	phl_out_centrality_5	phl_in_centrality_5	phl_betweenness_centrality_5	col_out_centrality_5	col_in_centrality_5	col_betweenness_centrality_5
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	...	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
BZIP9	0.0000000	0.0000000	0.0000000	0.00000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.000000000	...	1.0000000	0	0	0	0.7429245	0.88928151	0.08846168	0.00000000	0.000000000	0
AT3G43430	0.0000000	0.0000000	0.0000000	0.00000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.002298851	...	0.3216776	0	0	0	0.1450472	0.06831567	0.05658337	0.00000000	0.000000000	0
HAT7	0.0000000	0.0000000	0.0000000	0.03813559	0.3274336	0.9204856	0.0000000	0.0000000	0.0000000	0.052873563	...	0.0000000	0	0	0	0.0000000	0.00000000	0.00000000	0.00000000	0.000000000	0
PLT1	0.9708738	0.6024845	0.9763135	0.00000000	0.0000000	0.0000000	0.3452055	1.0000000	0.9765895	0.696551724	...	0.0000000	0	0	0	0.0000000	0.00000000	0.00000000	0.07563025	0.003424658	0
MYB36	0.0000000	0.0000000	0.0000000	0.00000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.050574713	...	0.0000000	0	0	0	0.0000000	0.00000000	0.00000000	0.00000000	0.000000000	0
GATA2	0.8284790	0.8012422	0.9985949	0.93644068	0.4424779	0.9416342	0.6739726	0.7235772	0.8874613	0.533333333	...	0.0000000	0	0	0	0.0000000	0.00000000	0.00000000	0.00000000	0.000000000	0

bc_rank <- data.frame(all=rowSums(sub),atri=rowSums(sub[,grep("^atri_",colnames(sub))]),tri=rowSums(sub[,grep("^tri_",colnames(sub))])
                     ,cor=rowSums(sub[,grep("^cor_",colnames(sub))]),end=rowSums(sub[,grep("^end_",colnames(sub))])
                     ,per=rowSums(sub[,grep("^per_",colnames(sub))]),pro=rowSums(sub[,grep("^pro_",colnames(sub))])
                     ,xyl=rowSums(sub[,grep("^xyl_",colnames(sub))]),phl=rowSums(sub[,grep("^phl_",colnames(sub))])
                     ,lrc=rowSums(sub[,grep("^lrc_",colnames(sub))]),col=rowSums(sub[,grep("^col_",colnames(sub))]))

bc_rank$GeneID <- wanted_TFs$GeneID[match(rownames(bc_rank),wanted_TFs$Name)]

head(bc_rank)

A data.frame: 6 x 12

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>
BZIP9	31.71447	0.000000	0.000000	0.000000000	0.006698565	8.04635576	11.94430365	0.03622764	11.6808854	0.000000	0.000000	AT5G24800
AT3G43430	29.50134	0.000000	0.000000	0.005375774	0.006698565	8.92414305	10.03369110	3.50786160	7.0235724	0.000000	0.000000	AT3G43430
HAT7	32.32415	7.054065	5.626399	8.996598123	4.910315271	0.02122024	0.00000000	0.00000000	0.0000000	4.433060	1.282490	AT5G15150
PLT1	22.94342	3.573806	0.000000	2.592117793	1.958605076	0.00000000	0.00000000	0.00000000	0.0000000	9.283443	5.535445	AT3G20840
MYB36	19.51309	0.000000	0.000000	3.965572845	11.648226873	3.89929436	0.00000000	0.00000000	0.0000000	0.000000	0.000000	AT5G57620
GATA2	30.67747	7.853413	5.533161	2.445113736	1.382593066	0.50181676	0.08967606	0.05936252	0.4627788	10.404863	1.944695	AT2G45050

Atrichoblast

atri_rank <- bc_rank[which(bc_rank$atri*2 > bc_rank$all),]%>% arrange(desc(atri))
atri_rank$GeneName <- rownames(atri_rank)

atri_rank

A data.frame: 23 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
TTG2	12.3474170	7.66213207	0.719954388	0.070557029	0.000000000	0.00000000	0.000000000	0.00000000	0.00000000	3.89477347	0.000000000	AT2G37260	TTG2
GL2	14.2419328	7.27837845	1.626446914	0.181043761	0.014787481	0.00000000	0.000000000	0.00000000	0.00000000	5.14127622	0.000000000	AT1G79840	GL2
ARR6	7.7177075	4.11180016	0.955805999	0.212809153	0.063140922	0.14839127	0.313312138	0.26656538	0.21309663	1.22045726	0.212328572	AT5G62920	ARR6
IAA14	7.1650824	3.72877840	1.769875146	0.208928016	0.000000000	0.15855665	0.160679903	0.28307186	0.01423876	0.61522335	0.225730302	AT4G14550	IAA14
CRF4	6.2276288	3.35877482	0.524429284	0.585671972	0.218314563	0.03942900	0.000000000	0.00000000	0.00000000	1.46415787	0.036851244	AT4G27950	CRF4
FIT	5.0340153	3.26960915	0.607038814	0.000000000	0.027304607	0.00000000	0.000000000	0.00000000	0.00000000	1.06085260	0.069210177	AT2G28160	FIT
OFP18	3.3119045	2.81438729	0.234187354	0.000000000	0.000000000	0.00000000	0.000000000	0.00000000	0.00000000	0.26332983	0.000000000	AT3G52540	OFP18
HB17	3.8871110	2.02438629	0.344483774	0.081348655	0.002772068	0.00000000	0.000000000	1.37770801	0.02278912	0.03362309	0.000000000	AT2G01430	HB17
KAN	2.8694390	1.82819812	0.293676699	0.198726790	0.003827751	0.00000000	0.000000000	0.00000000	0.00000000	0.45494039	0.090069225	AT5G16560	KAN
HMGB2	2.9869944	1.64816144	0.270499677	0.317272183	0.175544976	0.13586765	0.189008472	0.00000000	0.20179388	0.01128584	0.037560325	AT1G20693	HMGB2
BNQ3	2.7048052	1.63098882	0.142195361	0.010584029	0.000000000	0.00000000	0.000000000	0.00000000	0.00000000	0.88948145	0.031555541	AT3G47710	BNQ3
RMR1	1.8702933	1.47893413	0.006941857	0.054330139	0.120451875	0.02217495	0.074434994	0.00000000	0.05393853	0.01684529	0.042241552	AT5G66160	RMR1
NAC003	2.5357060	1.46480141	0.486766958	0.361627576	0.048125275	0.05701659	0.064006141	0.02969211	0.00000000	0.00000000	0.023669946	AT1G02220	NAC003
LBD25	2.6371840	1.40021981	0.000000000	0.942467752	0.167708473	0.07299161	0.053796393	0.00000000	0.00000000	0.00000000	0.000000000	AT3G27650	LBD25
NAC044	2.4560336	1.29601271	0.222985777	0.226052725	0.122099221	0.55301055	0.000000000	0.00000000	0.00000000	0.03587259	0.000000000	AT3G01600	NAC044
AIP2	2.3348480	1.24966354	0.200040411	0.117092771	0.147288489	0.06092371	0.191652548	0.00000000	0.23551228	0.07267386	0.060000383	AT5G20910	AIP2
HB24	1.6039612	1.06343666	0.298545105	0.000000000	0.000000000	0.02311951	0.111068082	0.00000000	0.00000000	0.10779189	0.000000000	AT2G18350	HB24
GIS3	0.6133367	0.51580487	0.020036430	0.000000000	0.000000000	0.00000000	0.000000000	0.00000000	0.00000000	0.07749544	0.000000000	AT1G68360	GIS3
AT2G18850	0.6161074	0.32152796	0.039073046	0.095207781	0.051728851	0.05215583	0.000000000	0.00000000	0.00000000	0.03455879	0.021855148	AT2G18850	AT2G18850
AT1G11490	0.4579165	0.24846511	0.186109392	0.000000000	0.000000000	0.00000000	0.000000000	0.00000000	0.00000000	0.02334204	0.000000000	AT1G11490	AT1G11490
NLP4	0.3071536	0.20900779	0.050454629	0.000000000	0.002880584	0.00000000	0.000000000	0.00000000	0.02510829	0.01060519	0.009097158	AT1G20640	NLP4
VFP5	0.2484859	0.13004092	0.000000000	0.060725022	0.000000000	0.02610951	0.007547768	0.00000000	0.00000000	0.01250398	0.011558680	AT5G05550	VFP5
LBD26	0.1023420	0.05473178	0.026534531	0.008146673	0.000000000	0.00000000	0.000000000	0.00000000	0.00000000	0.00785531	0.005073673	AT3G27940	LBD26

options(repr.plot.width=6, repr.plot.height=4)
ggplot(atri_rank[1:10,], aes(x=reorder(GeneName, atri, decreasing = FALSE),  y=atri)) + geom_point(size=4)+ 
  labs(title="Atrichoblast-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(atri_rank,"Atrichoblast_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- atri_rank %>% rownames(.)

p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Atrichoblast ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

q1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3])
q2 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3])
q3 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3])

q1 <- grid.grabExpr(draw(q1))
q2 <- grid.grabExpr(draw(q2))
q3 <- grid.grabExpr(draw(q3))

options(repr.plot.width=6, repr.plot.height=8)
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q1,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q2,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q3,nrow=2,rel_heights = c(1, 8)),nrow=3)

Trichoblast

tri_rank <- bc_rank[which(bc_rank$tri*2 > bc_rank$all),]%>% arrange(desc(tri))
tri_rank$GeneName <- rownames(tri_rank)

tri_rank

A data.frame: 21 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
AT3G53370	10.51332469	1.70510135	8.05305024	0.000000000	0.000000000	0.104263060	0.187801694	0.39737571	0.065732646	0.000000000	0.000000000	AT3G53370	AT3G53370
RHD6	11.22573826	3.85916008	7.36657818	0.000000000	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT1G66470	RHD6
WRKY65	8.65562962	1.38668348	4.36361919	0.011697506	0.197702082	0.000000000	0.000000000	0.00000000	0.000000000	1.613232755	1.082694618	AT1G29280	WRKY65
AT2G37120	6.29357775	0.93726369	3.76273932	0.104956333	0.177723679	0.101358153	0.007651672	0.80997145	0.192670974	0.199242466	0.000000000	AT2G37120	AT2G37120
tny	4.87767163	0.02922657	2.68223400	2.104674337	0.021349860	0.000000000	0.040186868	0.00000000	0.000000000	0.000000000	0.000000000	AT5G25810	tny
WRKY70	3.17128234	0.67219042	1.80645903	0.000000000	0.000000000	0.069130162	0.062103032	0.00000000	0.027210884	0.202466927	0.331721889	AT3G56400	WRKY70
AT4G39160	1.71652375	0.23754052	1.35605000	0.000000000	0.013519256	0.050989155	0.002159837	0.00000000	0.012332391	0.005735605	0.038196982	AT4G39160	AT4G39160
GL3	1.11211635	0.27241212	0.75079177	0.088912467	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT5G41315	GL3
RL6	0.65680222	0.05712800	0.55397474	0.000000000	0.000000000	0.000000000	0.000000000	0.00000000	0.015223153	0.030476328	0.000000000	AT1G75250	RL6
SUVR4	0.53241418	0.05081569	0.38315817	0.006152709	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.030393140	0.061894477	AT3G04380	SUVR4
bZIP23	0.64750855	0.01526386	0.32446092	0.054429708	0.016591570	0.039091346	0.025490165	0.00000000	0.140227289	0.017206816	0.014746876	AT2G16770	bZIP23
AT2G37000	0.34981598	0.06302639	0.24651267	0.008452697	0.000000000	0.028949327	0.002874891	0.00000000	0.000000000	0.000000000	0.000000000	AT2G37000	AT2G37000
BPC5	0.19254110	0.01605057	0.17649053	0.000000000	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT4G38910	BPC5
FRF1	0.18451513	0.00000000	0.10454207	0.000000000	0.029962971	0.007096562	0.011011325	0.00000000	0.005558887	0.007105468	0.019237845	AT3G59470	FRF1
ASG3	0.14783754	0.02176621	0.09874421	0.000000000	0.002870813	0.004888947	0.016119924	0.00000000	0.000000000	0.000000000	0.003447441	AT2G44980	ASG3
AT2G19380	0.11059372	0.00000000	0.07006888	0.000000000	0.003827751	0.000000000	0.004199394	0.00000000	0.019981905	0.000000000	0.012515794	AT2G19380	AT2G19380
AGL87	0.07581457	0.00000000	0.06124772	0.014566850	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT1G22590	AGL87
AT5G06420	0.09232181	0.00000000	0.04905872	0.000000000	0.022196290	0.003528344	0.002159837	0.01317961	0.002199011	0.000000000	0.000000000	AT5G06420	AT5G06420
PIL6	0.02984851	0.00000000	0.02984851	0.000000000	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT3G59060	PIL6
SUVH2	0.04308673	0.00000000	0.02496555	0.000000000	0.003827751	0.012133589	0.002159837	0.00000000	0.000000000	0.000000000	0.000000000	AT2G33290	SUVH2
LDL2	0.02982666	0.00000000	0.02047501	0.000000000	0.003827751	0.000000000	0.005523898	0.00000000	0.000000000	0.000000000	0.000000000	AT3G13682	LDL2

options(repr.plot.width=6, repr.plot.height=4)
ggplot(tri_rank[1:10,], aes(x=reorder(GeneName, tri, decreasing = FALSE),  y=tri)) + geom_point(size=4)+ 
  labs(title="Trichoblast-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(tri_rank,"Trichoblast_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

## Top20 only
tf_rank <- tri_rank %>% rownames(.)
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Trichoblast ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

q1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3])
q2 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3])
q3 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3])

q1 <- grid.grabExpr(draw(q1))
q2 <- grid.grabExpr(draw(q2))
q3 <- grid.grabExpr(draw(q3))

options(repr.plot.width=6, repr.plot.height=8)
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q1,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q2,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q3,nrow=2,rel_heights = c(1, 8)),nrow=3)

Cortex

cor_rank <- bc_rank[which(bc_rank$cor*2 > bc_rank$all),]%>% arrange(desc(cor))
cor_rank$GeneName <- rownames(cor_rank)

cor_rank

A data.frame: 15 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
JKD	8.2396085	0.0000000	0.0000000	5.358894391	2.720061321	0.000000000	0.000000000	0	0.000000000	0.00000000	0.160652759	AT5G03150	JKD
AT1G05710	6.1268177	0.0000000	0.0000000	4.414423648	1.670630776	0.036239355	0.005523898	0	0.000000000	0.00000000	0.000000000	AT1G05710	AT1G05710
AT1G72210	4.3978702	0.0000000	0.0000000	3.326076849	0.910060583	0.002360119	0.000000000	0	0.000000000	0.00000000	0.159372610	AT1G72210	AT1G72210
AT2G38300	3.1103756	0.0000000	0.0000000	2.585956800	0.413422249	0.000000000	0.000000000	0	0.000000000	0.00000000	0.110996559	AT2G38300	AT2G38300
JAZ6	2.0371642	0.1452226	0.0678334	1.522800605	0.175564432	0.055974677	0.048367608	0	0.004556113	0.00000000	0.016844717	AT1G72450	JAZ6
IDD4	1.7400502	0.0000000	0.0000000	1.348173725	0.379331910	0.000000000	0.000000000	0	0.000000000	0.00000000	0.012544599	AT2G02080	IDD4
LRP1	2.2433905	0.0000000	0.0000000	1.347436802	0.264705777	0.315998738	0.275261648	0	0.000000000	0.00000000	0.039987577	AT5G12330	LRP1
AT2G42660	1.5349520	0.0000000	0.0000000	1.337205769	0.000000000	0.000000000	0.000000000	0	0.000000000	0.00000000	0.197746268	AT2G42660	AT2G42660
ERF15	1.8318405	0.0000000	0.0000000	0.930032857	0.897259103	0.000000000	0.000000000	0	0.000000000	0.00000000	0.004548579	AT2G31230	ERF15
GLK2	1.0971457	0.0000000	0.0000000	0.791132583	0.002870813	0.078401208	0.169211121	0	0.055530008	0.00000000	0.000000000	AT5G44190	GLK2
ETR2	1.0127513	0.1235738	0.0000000	0.543954890	0.159645060	0.000000000	0.002874891	0	0.000000000	0.02489294	0.157809818	AT3G23150	ETR2
AT3G18960	0.1780973	0.0000000	0.0000000	0.157046686	0.000000000	0.005202625	0.000000000	0	0.000000000	0.00000000	0.015848014	AT3G18960	AT3G18960
AT2G46810	0.1968659	0.0000000	0.0000000	0.109681370	0.017377911	0.000000000	0.007909619	0	0.000000000	0.00000000	0.061896990	AT2G46810	AT2G46810
HSFC1	0.1935138	0.0000000	0.0000000	0.101184792	0.000000000	0.049610987	0.022321474	0	0.000000000	0.00000000	0.020396593	AT3G24520	HSFC1
AT3G04450	0.0125023	0.0000000	0.0000000	0.009973475	0.000000000	0.002528828	0.000000000	0	0.000000000	0.00000000	0.000000000	AT3G04450	AT3G04450

options(repr.plot.width=6, repr.plot.height=4)
ggplot(cor_rank[1:10,], aes(x=reorder(GeneName, cor, decreasing = FALSE),  y=cor)) + geom_point(size=4)+ 
  labs(title="Cortex-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(cor_rank,"Cortex_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- cor_rank %>% rownames(.)
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
#p4 <- plot_bc(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
#p8 <- plot_oc(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
#p12 <- plot_ic(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
#p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
#p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
#p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=9)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Cortex ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,nrow=3),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,nrow=3),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,nrow=3),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

## Top 10 only
tf_rank <- cor_rank %>% rownames(.)
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
#p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
#p4 <- plot_bc(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
#p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
#p8 <- plot_oc(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
#p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
#p12 <- plot_ic(tf_rank[16]) + plot_blank("Empty")+ plot_blank("Empty") + plot_blank("Empty")+ plot_blank("Empty")

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
#p3 <- grid.grabExpr(draw(p3))
#p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
#p7 <- grid.grabExpr(draw(p7))
#p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
#p11 <- grid.grabExpr(draw(p11))
#p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=5)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Cortex ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,nrow=2),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,nrow=2),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,nrow=2),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

q1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3])
q2 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3])
q3 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3])

q1 <- grid.grabExpr(draw(q1))
q2 <- grid.grabExpr(draw(q2))
q3 <- grid.grabExpr(draw(q3))

options(repr.plot.width=6, repr.plot.height=8)
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q1,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q2,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q3,nrow=2,rel_heights = c(1, 8)),nrow=3)

Endodermis

end_rank <- bc_rank[which(bc_rank$end*2 > bc_rank$all),]%>% arrange(desc(end))
end_rank$GeneName <- rownames(end_rank)

end_rank

A data.frame: 28 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
MYB36	19.51309408	0.00000000	0.000000000	3.96557285	11.64822687	3.899294360	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT5G57620	MYB36
MYB74	10.57984718	0.00000000	0.000000000	1.79339769	7.04070607	1.432723885	0.006848401	0.30617114	0.000000000	0.000000000	0.000000000	AT4G05100	MYB74
MYB68	7.95738692	0.00000000	0.000000000	1.46239238	6.21188064	0.283113908	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT5G65790	MYB68
SOM	6.91590076	0.31147119	0.000000000	1.00295479	4.67445399	0.647166979	0.234669337	0.00000000	0.045184473	0.000000000	0.000000000	AT1G03790	SOM
RAX2	5.93069125	0.35644137	0.034200666	1.20469945	3.94113761	0.353927368	0.000000000	0.00000000	0.000000000	0.040284772	0.000000000	AT2G36890	RAX2
BLJ	4.39923249	0.00000000	0.000000000	0.56360296	3.60218449	0.233445030	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT1G14580	BLJ
SCR	4.82269115	0.00000000	0.000000000	1.82554739	2.99714375	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT3G54220	SCR
TLP11	4.12025319	0.00000000	0.306172801	0.05100626	2.52513167	0.398776753	0.424594941	0.00000000	0.414570765	0.000000000	0.000000000	AT5G18680	TLP11
bZIP58	1.71582983	0.00000000	0.000000000	0.00000000	1.57178222	0.070008903	0.074038708	0.00000000	0.000000000	0.000000000	0.000000000	AT1G13600	bZIP58
BIB	1.26649446	0.00000000	0.000000000	0.07143260	1.19506186	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT3G45260	BIB
RVN	1.42678596	0.00000000	0.000000000	0.45433505	0.93783821	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.034612694	AT2G02070	RVN
MYB122	1.60487937	0.00000000	0.000000000	0.59238242	0.93547619	0.027500767	0.049519996	0.00000000	0.000000000	0.000000000	0.000000000	AT1G74080	MYB122
AT5G41920	0.94031384	0.00000000	0.000000000	0.08146738	0.85884646	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT5G41920	AT5G41920
AGL42	1.11052158	0.00000000	0.000000000	0.31676231	0.68784316	0.057906983	0.048009118	0.00000000	0.000000000	0.000000000	0.000000000	AT5G62165	AGL42
AT2G43140	0.77722377	0.00000000	0.000000000	0.10404195	0.67318183	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT2G43140	AT2G43140
ERF10	0.86308851	0.00000000	0.000000000	0.05534925	0.43302329	0.260388978	0.091257251	0.02306974	0.000000000	0.000000000	0.000000000	AT1G03800	ERF10
MYB70	0.63684752	0.03874335	0.003870674	0.10466428	0.34489513	0.025011951	0.042716418	0.00000000	0.000000000	0.022430115	0.054515598	AT2G23290	MYB70
TIFY8	0.38923606	0.00000000	0.000000000	0.00000000	0.27255040	0.005647096	0.000000000	0.00000000	0.000000000	0.074741062	0.036297497	AT4G32570	TIFY8
SIGA	0.33795007	0.00000000	0.000000000	0.03117682	0.25300666	0.035158070	0.011747163	0.00000000	0.000000000	0.000000000	0.006861356	AT1G64860	SIGA
AT3G07500	0.18825577	0.00000000	0.000000000	0.02836428	0.12076017	0.039131317	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT3G07500	AT3G07500
ERF73	0.14563708	0.00000000	0.000000000	0.00000000	0.11294226	0.007132812	0.008398788	0.00000000	0.000000000	0.008422647	0.008740567	AT1G72360	ERF73
FAR1	0.20489065	0.00000000	0.000000000	0.01706306	0.11233473	0.028666213	0.015927228	0.00000000	0.004556113	0.007105468	0.019237845	AT4G15090	FAR1
AT4G08455	0.14788778	0.00000000	0.021608650	0.00000000	0.07688595	0.025665449	0.019171624	0.00000000	0.004556113	0.000000000	0.000000000	AT4G08455	AT4G08455
TCP9	0.09886033	0.00000000	0.000000000	0.00000000	0.05387220	0.000000000	0.002159837	0.04282830	0.000000000	0.000000000	0.000000000	AT2G45680	TCP9
AT4G00390	0.06972115	0.00000000	0.000000000	0.02072502	0.04899612	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT4G00390	AT4G00390
AT3G01890	0.07500588	0.00000000	0.030223489	0.00000000	0.04154497	0.000000000	0.003237423	0.00000000	0.000000000	0.000000000	0.000000000	AT3G01890	AT3G01890
AT1G61970	0.07083045	0.00000000	0.000000000	0.00000000	0.03913811	0.004736403	0.009949176	0.00000000	0.000000000	0.000000000	0.017006763	AT1G61970	AT1G61970
NF-YA6	0.04945440	0.00000000	0.000000000	0.00000000	0.03325970	0.011769419	0.004425279	0.00000000	0.000000000	0.000000000	0.000000000	AT3G14020	NF-YA6

options(repr.plot.width=8, repr.plot.height=4)
ggplot(end_rank[1:10,], aes(x=reorder(GeneName, end, decreasing = FALSE),  y=end)) + geom_point(size=4)+ 
  labs(title="Endodermis-specific TF Prioritization",x="", y = "Combined centrality score (betweeness, out and in degree)")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(end_rank,"Endodermis_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- end_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Endodermis ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

tf_rank <- end_rank %>% rownames(.)
# Max 10
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
#p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
#p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
#p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
#p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
#p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
#p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
#p3 <- grid.grabExpr(draw(p3))
#p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
#p7 <- grid.grabExpr(draw(p7))
#p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
#p11 <- grid.grabExpr(draw(p11))
#p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=5)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Endodermis ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,nrow=2),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,nrow=2),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,nrow=2),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

q1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5]) + plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10]) 
q2 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5]) + plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10]) 
q3 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5]) + plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10]) 

q1 <- grid.grabExpr(draw(q1))
q2 <- grid.grabExpr(draw(q2))
q3 <- grid.grabExpr(draw(q3))

options(repr.plot.width=16, repr.plot.height=8)
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q1,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q2,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q3,nrow=2,rel_heights = c(1, 8)),nrow=3)

Pericycle

per_rank <- bc_rank[which(bc_rank$per*2 > bc_rank$all),]%>% arrange(desc(per))
per_rank$GeneName <- rownames(per_rank)

per_rank

A data.frame: 21 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
MYBC1	13.63866508	0.00000000	0.00000000	0.45338769	2.143205062	9.00661080	1.794287688	0.00000000	0.24117383	0.000000000	0.000000000	AT2G40970	MYBC1
LBD16	7.82135600	0.00000000	0.00000000	0.11658626	0.174778877	4.15706614	3.073922149	0.00000000	0.29900256	0.000000000	0.000000000	AT2G42430	LBD16
LBD14	4.16771007	0.00000000	0.00000000	0.05761273	0.036996037	3.83815074	0.234950563	0.00000000	0.00000000	0.000000000	0.000000000	AT2G31310	LBD14
SAP	4.26388507	0.13255543	0.00000000	0.24218919	0.160135518	3.62269558	0.106309359	0.00000000	0.00000000	0.000000000	0.000000000	AT5G35770	SAP
NUC	5.55906804	0.00000000	0.00000000	0.47189966	0.000000000	3.61382214	0.649976803	0.00000000	0.81993876	0.000000000	0.003430678	AT5G44160	NUC
AT3G21330	5.65980461	0.16912726	0.00000000	0.55388195	0.565696008	3.42748431	0.943615086	0.00000000	0.00000000	0.000000000	0.000000000	AT3G21330	AT3G21330
IDD11	4.27563103	0.00000000	0.00000000	0.00000000	0.000000000	3.28891496	0.986716077	0.00000000	0.00000000	0.000000000	0.000000000	AT3G13810	IDD11
MGP	4.46285753	0.00000000	0.00000000	0.74208303	0.133264999	3.05244123	0.535068272	0.00000000	0.00000000	0.000000000	0.000000000	AT1G03840	MGP
AT2G14880	3.66710161	0.54757196	0.01751162	0.12005362	0.305700697	2.38140484	0.272684904	0.00000000	0.00000000	0.022173961	0.000000000	AT2G14880	AT2G14880
GATA23	2.88800573	0.00000000	0.00000000	0.00000000	0.009735856	2.13472979	0.743540082	0.00000000	0.00000000	0.000000000	0.000000000	AT5G26930	GATA23
AT2G20100	2.96560299	0.00000000	0.00000000	0.02380195	0.002870813	1.66967546	0.602056199	0.66719857	0.00000000	0.000000000	0.000000000	AT2G20100	AT2G20100
GRF9	1.10320281	0.15027438	0.03851057	0.01842617	0.052197705	0.59273823	0.251055752	0.00000000	0.00000000	0.000000000	0.000000000	AT2G45480	GRF9
NAGS2	0.84934059	0.04517679	0.00000000	0.03147657	0.042711632	0.43134050	0.188716445	0.01319046	0.07687075	0.019857445	0.000000000	AT4G37670	NAGS2
LBD29	0.42833314	0.00000000	0.00000000	0.00000000	0.000000000	0.22043352	0.207899617	0.00000000	0.00000000	0.000000000	0.000000000	AT3G58190	LBD29
AT1G78930	0.16574737	0.00000000	0.00000000	0.00000000	0.003827751	0.12865794	0.027526077	0.00000000	0.00000000	0.005735605	0.000000000	AT1G78930	AT1G78930
BOP2	0.16720625	0.00000000	0.00000000	0.00000000	0.000000000	0.11416451	0.053041737	0.00000000	0.00000000	0.000000000	0.000000000	AT2G41370	BOP2
AT4G31060	0.09506851	0.00000000	0.00000000	0.00000000	0.000000000	0.06726357	0.006359232	0.00000000	0.01134436	0.010101348	0.000000000	AT4G31060	AT4G31060
AT3G61550	0.10490425	0.00000000	0.00000000	0.04445623	0.000000000	0.05745213	0.000000000	0.00000000	0.00000000	0.002995879	0.000000000	AT3G61550	AT3G61550
AT1G24210	0.09631176	0.00000000	0.00000000	0.00000000	0.004784689	0.05536655	0.036160516	0.00000000	0.00000000	0.000000000	0.000000000	AT1G24210	AT1G24210
AT1G68030	0.03309547	0.00000000	0.00000000	0.00000000	0.003827751	0.01802314	0.011244577	0.00000000	0.00000000	0.000000000	0.000000000	AT1G68030	AT1G68030
PRR3	0.01033083	0.00000000	0.00000000	0.00000000	0.000000000	0.01033083	0.000000000	0.00000000	0.00000000	0.000000000	0.000000000	AT5G60100	PRR3

options(repr.plot.width=6, repr.plot.height=4)
ggplot(per_rank[1:10,], aes(x=reorder(GeneName, per, decreasing = FALSE),  y=per)) + geom_point(size=4)+ 
  labs(title="Pericycle-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(per_rank,"Pericycle_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- per_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Pericycle ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

q1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3])
q2 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3])
q3 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3])

q1 <- grid.grabExpr(draw(q1))
q2 <- grid.grabExpr(draw(q2))
q3 <- grid.grabExpr(draw(q3))

options(repr.plot.width=6, repr.plot.height=8)
plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q1,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q2,nrow=2,rel_heights = c(1, 8))
          ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),q3,nrow=2,rel_heights = c(1, 8)),nrow=3)

Procambium

pro_rank <- bc_rank[which(bc_rank$pro*2 > bc_rank$all),]%>% arrange(desc(pro))
pro_rank$GeneName <- rownames(pro_rank)

pro_rank

A data.frame: 14 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
IAA28	13.7612975	0.1894170	0.073915073	0.532529533	0.318490933	0.71275472	7.06383265	0.8391969	4.031160765	0.000000000	0.000000000	AT5G25890	IAA28
ABO3	11.6856561	0.2128955	0.009809774	0.029197080	0.111201150	4.32810648	6.13141112	0.1753614	0.687673543	0.000000000	0.000000000	AT1G66600	ABO3
IDD14	6.2086688	0.0000000	0.000000000	0.000000000	0.070188675	1.55017977	3.11049066	0.3265915	1.151218201	0.000000000	0.000000000	AT1G68130	IDD14
HAT9	5.5698182	0.0000000	0.020664199	0.025932810	0.011483254	2.26225702	3.01849618	0.0000000	0.230984762	0.000000000	0.000000000	AT2G22800	HAT9
ERF12	4.6778600	0.0000000	0.000000000	0.000000000	0.015311005	1.64645212	2.54169873	0.0000000	0.474398163	0.000000000	0.000000000	AT1G28360	ERF12
AT1G75490	2.1074593	0.0000000	0.000000000	0.000000000	0.000000000	0.13661200	1.66707941	0.0000000	0.303767886	0.000000000	0.000000000	AT1G75490	AT1G75490
HB18	1.8519702	0.0000000	0.000000000	0.000000000	0.004784689	0.18242039	1.47025793	0.0000000	0.194507235	0.000000000	0.000000000	AT1G70920	HB18
AT2G40200	2.0720869	0.0000000	0.000000000	0.005375774	0.000000000	0.23731271	1.42836811	0.0000000	0.401030320	0.000000000	0.000000000	AT2G40200	AT2G40200
NAC080	1.8683555	0.0000000	0.000000000	0.000000000	0.006698565	0.33656391	1.23911258	0.0000000	0.270348053	0.008422647	0.007209796	AT5G07680	NAC080
SHY2	1.9905026	0.0000000	0.000000000	0.000000000	0.000000000	0.08385465	1.21982592	0.2089959	0.477826070	0.000000000	0.000000000	AT1G04240	SHY2
AT4G20970	1.0004653	0.0000000	0.000000000	0.000000000	0.002870813	0.23559663	0.74337637	0.0000000	0.018621479	0.000000000	0.000000000	AT4G20970	AT4G20970
AT4G27240	0.1608753	0.0000000	0.000000000	0.024544651	0.000000000	0.02317468	0.10327470	0.0000000	0.006462585	0.000000000	0.003418636	AT4G27240	AT4G27240
FRS12	0.1570577	0.0000000	0.000000000	0.000000000	0.051518140	0.01373056	0.08500051	0.0000000	0.000000000	0.000000000	0.006808466	AT5G18960	FRS12
DAR6	0.0945977	0.0000000	0.000000000	0.000000000	0.003827751	0.00000000	0.05792793	0.0000000	0.000000000	0.000000000	0.032842019	AT5G66620	DAR6

options(repr.plot.width=6, repr.plot.height=4)
ggplot(pro_rank[1:10,], aes(x=reorder(GeneName, pro, decreasing = FALSE),  y=pro)) + geom_point(size=4)+ 
  labs(title="Procambium-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(pro_rank,"Procambium_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- pro_rank %>% rownames(.)
# Max 30
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_blank(tf_rank[15])

p4 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p5 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p6 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_blank(tf_rank[15])

p7 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p8 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p9 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_blank(tf_rank[15])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=10)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Procambium ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,nrow=3),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p4,p5,p6,nrow=3),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p7,p8,p9,nrow=3),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Phloem

phl_rank <- bc_rank[which(bc_rank$phl*2 > bc_rank$all),]%>% arrange(desc(phl))
phl_rank$GeneName <- rownames(phl_rank)

phl_rank

A data.frame: 55 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
APL	18.6229618	0.00000000	0.000000000	0.00000000	0.000000000	1.265907639	2.495922354	1.641531332	13.21960051	0.000000000	0.000000000	AT1G79430	APL
OBP2	19.5573254	0.00000000	0.000000000	0.00000000	0.000000000	3.989014385	4.430147678	0.000000000	11.13816336	0.000000000	0.000000000	AT1G07640	OBP2
AT3G12730	10.4310407	0.00000000	0.000000000	0.00000000	0.000000000	0.929487640	0.211938642	0.000000000	9.28961440	0.000000000	0.000000000	AT3G12730	AT3G12730
DAR2	10.5373864	0.00000000	0.000000000	0.00000000	0.000000000	0.202522452	1.263778973	0.465563161	8.60552182	0.000000000	0.000000000	AT2G39830	DAR2
AT2G03500	9.9853427	0.00000000	0.000000000	0.00000000	0.000000000	0.766498483	0.437397254	0.235024550	8.54642243	0.000000000	0.000000000	AT2G03500	AT2G03500
DOF6	11.3168709	0.00000000	0.000000000	0.00000000	0.000000000	0.581244409	0.354212657	2.053785270	8.32762854	0.000000000	0.000000000	AT3G45610	DOF6
NAC057	7.2796075	0.00000000	0.000000000	0.00000000	0.000000000	0.050659720	0.163432843	0.520738836	6.54477611	0.000000000	0.000000000	AT3G17730	NAC057
HCA2	7.1364223	0.00000000	0.000000000	0.00000000	0.000000000	0.141976234	0.138924581	0.482277297	6.37324420	0.000000000	0.000000000	AT5G62940	HCA2
AT2G44940	9.1179088	0.00000000	0.000000000	0.00000000	0.060890108	1.202172996	1.547308685	0.000000000	6.30753700	0.000000000	0.000000000	AT2G44940	AT2G44940
AT5G41380	5.6560690	0.00000000	0.000000000	0.00000000	0.000000000	0.016591848	0.038171909	0.000000000	5.60130528	0.000000000	0.000000000	AT5G41380	AT5G41380
AT2G28810	8.7586296	0.00000000	0.000000000	0.00000000	0.000000000	1.228165412	1.953536345	0.026359215	5.55056866	0.000000000	0.000000000	AT2G28810	AT2G28810
AT4G37180	6.4796802	0.00000000	0.013971734	0.00000000	0.041868597	0.179018067	0.245277121	0.176848121	5.43093195	0.093830048	0.297934576	AT4G37180	AT4G37180
NAC020	5.6712996	0.00000000	0.000000000	0.00000000	0.000000000	0.072410927	0.353897183	0.000000000	5.24499146	0.000000000	0.000000000	AT1G54330	NAC020
AT1G49560	6.6056960	0.29307780	0.000000000	0.34341364	0.363703622	0.010438153	0.143665940	0.346042646	4.60125754	0.219171301	0.284925398	AT1G49560	AT1G49560
REM22	4.3919838	0.00000000	0.000000000	0.00000000	0.000000000	0.003528344	0.163612008	0.000000000	4.22484340	0.000000000	0.000000000	AT3G17010	REM22
DOF2.4	7.5188911	0.00000000	0.000000000	0.00000000	0.000000000	1.214999301	2.126565035	0.000000000	4.17732673	0.000000000	0.000000000	AT2G37590	DOF2.4
AT1G63820	5.2567575	0.00000000	0.000000000	0.00000000	0.006599819	0.753516474	0.408906313	0.000000000	4.08773490	0.000000000	0.000000000	AT1G63820	AT1G63820
AT5G02460	4.2293313	0.00000000	0.000000000	0.00000000	0.000000000	0.086242755	0.192938556	0.000000000	3.95015003	0.000000000	0.000000000	AT5G02460	AT5G02460
NAC2	4.4214728	0.00000000	0.000000000	0.00000000	0.000000000	0.192505456	0.279765561	0.000000000	3.94920179	0.000000000	0.000000000	AT3G15510	NAC2
CRF1	5.4657129	0.32106873	0.085120744	0.25018568	0.236845702	0.113509568	0.097596610	0.000000000	3.88966379	0.359865948	0.111856089	AT4G11140	CRF1
AT2G28510	7.2298500	0.00000000	0.000000000	0.01916888	0.266916043	0.977086168	2.130936764	0.046193764	3.78954843	0.000000000	0.000000000	AT2G28510	AT2G28510
GATA20	3.7301836	0.00000000	0.000000000	0.00000000	0.000000000	0.011729585	0.063373751	0.000000000	3.65508027	0.000000000	0.000000000	AT2G18380	GATA20
FLP	5.8700418	0.35716057	0.000000000	0.24435013	0.044307776	0.062689989	0.177747204	0.000000000	3.25311039	1.441740728	0.288935003	AT1G14350	FLP
AT5G63700	3.5044795	0.00000000	0.000000000	0.48634884	0.000000000	0.000000000	0.008172904	0.000000000	2.87521984	0.000000000	0.134737917	AT5G63700	AT5G63700
bZIP44	5.3854736	0.05706981	0.056660403	0.05859384	0.165572064	1.278097977	0.763682922	0.204511899	2.80128473	0.000000000	0.000000000	AT1G75390	bZIP44
AT1G26790	3.1080048	0.00000000	0.000000000	0.00000000	0.000000000	0.009464148	0.560694982	0.000000000	2.53784572	0.000000000	0.000000000	AT1G26790	AT1G26790
KNAT2	2.8581101	0.00000000	0.000000000	0.04074271	0.286151817	0.000000000	0.065623255	0.000000000	2.46559236	0.000000000	0.000000000	AT1G70510	KNAT2
AT1G28310	4.3372161	0.00000000	0.000000000	0.00000000	0.638640043	0.439318279	0.974659200	0.000000000	2.28459859	0.000000000	0.000000000	AT1G28310	AT1G28310
AGL15	2.1996263	0.00000000	0.000000000	0.00000000	0.004784689	0.007080358	0.032419989	0.000000000	2.15534125	0.000000000	0.000000000	AT5G13790	AGL15
WRKY21	3.5813006	0.00000000	0.000000000	0.02874896	0.100010201	0.410844223	0.737779073	0.000000000	2.08822908	0.094816356	0.120872667	AT2G30590	WRKY21
bZIP19	3.5462178	0.15967329	0.008611306	0.02702090	0.098726261	0.415048119	0.827613182	0.000000000	2.00952469	0.000000000	0.000000000	AT4G35040	bZIP19
NAC086	1.9759534	0.00000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	1.97595337	0.000000000	0.000000000	AT5G17260	NAC086
WRKY32	2.9820206	0.00000000	0.000000000	0.00000000	0.051972540	0.471563276	0.443035057	0.000000000	1.92765707	0.043956760	0.043835858	AT4G30935	WRKY32
VOZ1	3.0449548	0.00000000	0.000000000	0.00000000	0.034747660	0.401000225	0.470980827	0.179972277	1.89158202	0.027375281	0.039296518	AT1G28520	VOZ1
SVP	1.9221346	0.04802107	0.000000000	0.01998232	0.000000000	0.066273969	0.058390856	0.000000000	1.71697942	0.000000000	0.012486989	AT2G22540	SVP
CDF3	2.8630256	0.00000000	0.000000000	0.00000000	0.005741627	0.381991751	0.837228711	0.000000000	1.62410873	0.013954783	0.000000000	AT3G47500	CDF3
AS1	3.1581149	0.01039264	0.000000000	0.09551257	0.000000000	0.161418109	0.517381856	0.738659628	1.58558170	0.000000000	0.049168432	AT2G37630	AS1
ROS4	2.6253138	0.01040465	0.037340619	0.02938081	0.063401895	0.182997662	0.436897616	0.362612906	1.44570769	0.029817125	0.026752804	AT3G14980	ROS4
AT1G47570	2.4471694	0.02186979	0.060678768	0.04215738	0.002870813	0.175520259	0.126976638	0.745774195	1.23502201	0.024913687	0.011385850	AT1G47570	AT1G47570
NAC045	1.1691726	0.00000000	0.000000000	0.00000000	0.000000000	0.067350090	0.311972555	0.000000000	0.78984996	0.000000000	0.000000000	AT3G03200	NAC045
ARF11	1.1526639	0.00000000	0.000000000	0.00000000	0.031615490	0.000000000	0.000000000	0.471078970	0.64996943	0.000000000	0.000000000	AT2G46530	ARF11
WOX2	0.6153504	0.00000000	0.000000000	0.00000000	0.025617433	0.000000000	0.000000000	0.000000000	0.58973295	0.000000000	0.000000000	AT5G59340	WOX2
AT1G64530	1.0090791	0.05823341	0.087313508	0.08195501	0.083935590	0.027391445	0.033021114	0.000000000	0.57040078	0.008352434	0.058475786	AT1G64530	AT1G64530
ET2	0.6569860	0.00000000	0.000000000	0.00000000	0.000000000	0.059093920	0.094088184	0.000000000	0.50380388	0.000000000	0.000000000	AT5G56780	ET2
ARF18	0.7570837	0.00000000	0.000000000	0.03374005	0.000000000	0.105285718	0.198597938	0.009890134	0.39052540	0.014467090	0.004577384	AT3G61830	ARF18
MBF1A	0.7251328	0.01657087	0.089909864	0.00000000	0.014507098	0.029238589	0.105514812	0.000000000	0.38242405	0.000000000	0.086967539	AT2G42680	MBF1A
AT1G02030	0.4705940	0.00000000	0.020947177	0.00000000	0.035651439	0.000000000	0.000000000	0.000000000	0.37959144	0.034403983	0.000000000	AT1G02030	AT1G02030
AGL80	0.3893251	0.00000000	0.000000000	0.00000000	0.003827751	0.008295924	0.046711894	0.000000000	0.32373516	0.000000000	0.006754346	AT5G48670	AGL80
AT1G58220	0.4572639	0.00000000	0.003426918	0.00000000	0.024320089	0.004567734	0.103321366	0.000000000	0.27050640	0.002995879	0.048125521	AT1G58220	AT1G58220
AT1G09710	0.4063927	0.00000000	0.000000000	0.01305040	0.004784689	0.060749649	0.092110582	0.000000000	0.21031228	0.018576679	0.006808466	AT1G09710	AT1G09710
SPL13A	0.3142244	0.00000000	0.000000000	0.00000000	0.000000000	0.000000000	0.027184817	0.000000000	0.20822558	0.059474512	0.019339473	AT5G50570	SPL13A
AT3G46070	0.1541026	0.00000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.14425743	0.009845194	0.000000000	AT3G46070	AT3G46070
TCP24	0.1178094	0.00000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.11780944	0.000000000	0.000000000	AT1G30210	TCP24
SPL10	0.1488705	0.00000000	0.000000000	0.00000000	0.005741627	0.002528828	0.016148697	0.000000000	0.08832757	0.008475331	0.027648459	AT1G27370	SPL10
AT4G12850	0.1051155	0.00000000	0.030988627	0.01018749	0.002870813	0.000000000	0.000000000	0.000000000	0.06106859	0.000000000	0.000000000	AT4G12850	AT4G12850

options(repr.plot.width=6, repr.plot.height=4)
ggplot(phl_rank[1:10,], aes(x=reorder(GeneName, phl, decreasing = FALSE),  y=phl)) + geom_point(size=4)+ 
  labs(title="Phloem-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(phl_rank,"Phloem_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- phl_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Phloem ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Xylem

xyl_rank <- bc_rank[which(bc_rank$xyl*2 > bc_rank$all),]%>% arrange(desc(xyl))
xyl_rank$GeneName <- rownames(xyl_rank)

xyl_rank

A data.frame: 96 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
AT1G68810	15.740776	0.00000000	1.24410788	0.00000000	0.000000000	0.006039273	0.100109511	12.388537	2.00198178	0.000000000	0.00000000	AT1G68810	AT1G68810
MYB46	10.975484	0.00000000	0.00000000	0.00000000	0.000000000	0.000000000	0.000000000	10.975484	0.00000000	0.000000000	0.00000000	AT5G12870	MYB46
VND2	12.618169	0.00000000	1.12948393	0.00000000	0.000000000	0.000000000	0.006818368	10.346859	1.13500836	0.000000000	0.00000000	AT4G36160	VND2
ATHB-15	15.439924	0.00000000	0.00000000	0.00000000	0.000000000	0.318966129	3.657313301	9.677060	1.78658486	0.000000000	0.00000000	AT1G52150	ATHB-15
DOF2	13.224178	0.00000000	1.36575019	0.20072993	1.487350226	0.000000000	0.002870999	9.492840	0.59625901	0.017206816	0.06117147	AT3G21270	DOF2
MYB83	9.307381	0.00000000	0.00000000	0.00000000	0.000000000	0.000000000	0.000000000	9.307381	0.00000000	0.000000000	0.00000000	AT3G08500	MYB83
VND7	9.284049	0.00000000	0.00000000	0.00000000	0.000000000	0.000000000	0.000000000	9.284049	0.00000000	0.000000000	0.00000000	AT1G71930	VND7
AT1G66810	10.071416	0.17251857	0.52012453	0.00000000	0.000000000	0.000000000	0.000000000	9.281125	0.00000000	0.097647795	0.00000000	AT1G66810	AT1G66810
IAA6	9.310356	0.00000000	0.00000000	0.00000000	0.000000000	0.000000000	0.000000000	9.218519	0.09183673	0.000000000	0.00000000	AT1G52830	IAA6
VND4	9.097454	0.00000000	0.00000000	0.00000000	0.000000000	0.000000000	0.000000000	9.097454	0.00000000	0.000000000	0.00000000	AT1G12260	VND4
ZHD3	8.759365	0.00000000	0.00000000	0.00000000	0.000000000	0.000000000	0.000000000	8.759365	0.00000000	0.000000000	0.00000000	AT2G02540	ZHD3
VND5	8.507632	0.00000000	0.00000000	0.00000000	0.000000000	0.000000000	0.000000000	8.507632	0.00000000	0.000000000	0.00000000	AT1G62700	VND5
IAA8	12.840884	0.12171432	0.00000000	0.05909814	0.041909435	0.606687493	1.171022730	8.427339	1.16907518	0.813833815	0.43020350	AT2G22670	IAA8
LBD18	8.776155	0.00000000	0.00000000	0.00000000	0.000000000	0.000000000	0.000000000	8.407800	0.00000000	0.005490096	0.36286446	AT2G45420	LBD18
XND1	8.148155	0.00000000	0.00000000	0.00000000	0.000000000	0.000000000	0.011712843	8.046987	0.08945578	0.000000000	0.00000000	AT5G64530	XND1
VND1	7.979560	0.00000000	0.07852107	0.00000000	0.000000000	0.000000000	0.000000000	7.875529	0.02551020	0.000000000	0.00000000	AT2G18060	VND1
VND3	8.880562	0.00000000	0.04754762	0.00000000	0.000000000	0.000000000	0.012799787	7.566564	1.25365063	0.000000000	0.00000000	AT5G66300	VND3
JLO	7.504988	0.00000000	0.00000000	0.00000000	0.000000000	0.000000000	0.000000000	7.446554	0.00000000	0.016095282	0.04233802	AT4G00220	JLO
BHLH32	14.563942	0.05151862	1.57463777	0.19854996	0.099917999	3.786611414	0.983844784	7.317298	0.55156347	0.000000000	0.00000000	AT3G25710	BHLH32
WLIM2a	13.154491	0.54881186	3.79112766	0.01017919	0.047321919	0.123602481	0.748725324	6.996558	0.88816433	0.000000000	0.00000000	AT2G39900	WLIM2a
PHB	12.040463	0.00000000	0.00000000	0.00000000	0.000000000	0.237371742	2.523777767	6.965859	2.17455922	0.021402373	0.11749355	AT2G34710	PHB
IAA11	12.550370	0.08622047	0.12355248	0.00000000	0.007556757	1.273799727	1.428987033	6.711306	2.76585491	0.075056372	0.07803629	AT4G28640	IAA11
NAC075	12.001041	0.00000000	0.00000000	0.00000000	0.000000000	0.118185790	0.244830586	6.695381	4.88880550	0.008047641	0.04579085	AT4G29230	NAC075
AT1G68200	6.677427	0.00000000	0.00000000	0.00000000	0.000000000	0.000000000	0.000000000	6.677427	0.00000000	0.000000000	0.00000000	AT1G68200	AT1G68200
KNAT7	12.366370	2.67116714	0.12904688	0.56274129	0.625392411	0.276956970	0.598733886	6.660096	0.02561893	0.140758385	0.67585785	AT1G62990	KNAT7
WOX13	12.067411	0.00000000	0.00000000	0.00000000	0.026334587	2.513367403	1.624525102	6.606082	1.29710156	0.000000000	0.00000000	AT4G35550	WOX13
HB31	6.337598	0.00000000	0.00000000	0.00000000	0.000000000	0.000000000	0.000000000	6.337598	0.00000000	0.000000000	0.00000000	AT1G14440	HB31
NAC010	9.937748	0.00000000	0.00000000	0.00000000	0.000000000	0.000000000	0.000000000	5.810148	0.33068220	1.240734700	2.55618313	AT1G28470	NAC010
IAA31	11.124454	0.00000000	2.20788198	0.00000000	0.000000000	0.009141689	0.931777856	5.776671	2.19898169	0.000000000	0.00000000	AT3G17600	IAA31
HAT14	6.959710	0.00000000	0.00000000	0.00000000	0.000000000	0.064327093	0.188703542	5.623012	1.08366770	0.000000000	0.00000000	AT5G06710	HAT14
...	...	...	...	...	...	...	...	...	...	...	...	...	...
IAA10	2.2761996	0.145945811	0.049826436	0.00000000	0.063554539	0.044499276	0.114979744	1.27450484	0.299186129	0.102669818	0.18103304	AT1G04100	IAA10
PIF4	1.2094936	0.000000000	0.000000000	0.00000000	0.004784689	0.000000000	0.000000000	1.17621376	0.028495113	0.000000000	0.00000000	AT2G43010	PIF4
RHA1	1.9875550	0.103293527	0.179681819	0.03796981	0.151906575	0.046512950	0.148716393	1.17183134	0.045426724	0.008047641	0.09416821	AT5G45710	RHA1
AT5G18090	1.3263913	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	1.14236258	0.120090188	0.048215459	0.01572303	AT5G18090	AT5G18090
AT1G21780	2.2515365	0.000000000	0.020976885	0.00000000	0.034143770	0.009456681	0.057540383	1.12693603	0.090826685	0.353475883	0.55818016	AT1G21780	AT1G21780
HB34	1.3515069	0.034737721	0.010368775	0.00000000	0.019862568	0.013903040	0.054804112	1.11961032	0.090281938	0.000000000	0.00793841	AT3G28920	HB34
SPL2	2.0435743	0.018894852	0.000000000	0.00000000	0.010620358	0.109835604	0.304257605	1.11948664	0.297317341	0.080194770	0.10296710	AT5G43270	SPL2
AT5G03500	1.6575738	0.058704714	0.213082558	0.00000000	0.132206428	0.019820263	0.029805077	1.09844821	0.057508895	0.026325461	0.02167218	AT5G03500	AT5G03500
AT3G22100	1.5387316	0.000000000	0.006941857	0.00000000	0.000000000	0.018023138	0.014594418	1.06322604	0.408444118	0.007105468	0.02039659	AT3G22100	AT3G22100
AT1G51200	1.9263451	0.000000000	0.064743423	0.00000000	0.040979392	0.071402403	0.436821987	1.05976931	0.136414491	0.000000000	0.11621412	AT1G51200	AT1G51200
AT1G24040	1.0093640	0.000000000	0.000000000	0.00000000	0.002870813	0.000000000	0.000000000	1.00349735	0.000000000	0.002995879	0.00000000	AT1G24040	AT1G24040
IWS1	1.6326421	0.089205871	0.047996690	0.03788680	0.116301168	0.030334341	0.124837727	0.94627349	0.092484494	0.000000000	0.14732156	AT1G32130	IWS1
AT1G26300	1.7266014	0.087124887	0.157036812	0.08665441	0.161443425	0.038088967	0.070749444	0.93264995	0.101221580	0.042491354	0.04914057	AT1G26300	AT1G26300
GT-1	1.6468655	0.000000000	0.102939419	0.01760136	0.083759454	0.093574124	0.216666653	0.89370839	0.222225816	0.000000000	0.01639030	AT1G13450	GT-1
AT5G06770	1.1767088	0.014699067	0.091938705	0.03260813	0.070863131	0.000000000	0.005034728	0.88094842	0.041496599	0.000000000	0.03912002	AT5G06770	AT5G06770
MYB25	1.2989850	0.047367786	0.000000000	0.05828470	0.077750444	0.207520597	0.039220558	0.84874511	0.020095782	0.000000000	0.00000000	AT2G39880	MYB25
AL2	1.4679700	0.000000000	0.085056479	0.02852650	0.072986023	0.021832074	0.071542529	0.80473242	0.178881880	0.000000000	0.20441209	AT3G11200	AL2
JMJ14	1.5057936	0.052093312	0.058505892	0.01836174	0.030805190	0.045206610	0.047611616	0.79877611	0.084254665	0.152599993	0.21757852	AT4G20400	JMJ14
FBH2	1.0955629	0.037892296	0.000000000	0.01351045	0.029638329	0.022029934	0.010774162	0.74129668	0.173495992	0.012584920	0.05434009	AT4G09180	FBH2
RFI2	1.3929314	0.000000000	0.123497053	0.03637426	0.087273474	0.035972385	0.150545647	0.73209023	0.122417092	0.003927655	0.10083363	AT2G47700	RFI2
ARF1	1.3087496	0.056879133	0.126052676	0.03406550	0.047283056	0.021159261	0.094533372	0.66034563	0.158353317	0.008352434	0.10172524	AT1G59750	ARF1
GIF3	0.6682404	0.000000000	0.000000000	0.00000000	0.036867189	0.006830284	0.028454822	0.59608811	0.000000000	0.000000000	0.00000000	AT4G00850	GIF3
BZIP24	0.6897504	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.056222886	0.48148666	0.152040816	0.000000000	0.00000000	AT3G51960	BZIP24
AT3G45880	0.5323593	0.060966050	0.000000000	0.00000000	0.012440191	0.041100061	0.000000000	0.40426226	0.013590772	0.000000000	0.00000000	AT3G45880	AT3G45880
AT5G25470	0.7261561	0.005428218	0.000000000	0.06988506	0.000000000	0.000000000	0.000000000	0.38261960	0.045498588	0.153038939	0.06968570	AT5G25470	AT5G25470
AGL64	0.2736777	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.27367770	0.000000000	0.000000000	0.00000000	AT1G29962	AGL64
ARR15	0.2637723	0.000000000	0.000000000	0.01017919	0.000000000	0.007766728	0.013278628	0.20431648	0.028231293	0.000000000	0.00000000	AT1G74890	ARR15
ORC1A	0.1222350	0.000000000	0.000000000	0.00000000	0.000000000	0.023672282	0.002874891	0.09568786	0.000000000	0.000000000	0.00000000	AT4G14700	ORC1A
EMB93	0.1269562	0.000000000	0.000000000	0.00000000	0.017948692	0.016827075	0.002159837	0.07255298	0.004980622	0.000000000	0.01248699	AT2G03050	EMB93
AT4G08250	0.1061213	0.000000000	0.006765816	0.00000000	0.036670900	0.000000000	0.000000000	0.06268456	0.000000000	0.000000000	0.00000000	AT4G08250	AT4G08250

options(repr.plot.width=6, repr.plot.height=4)
ggplot(xyl_rank[1:10,], aes(x=reorder(GeneName, xyl, decreasing = FALSE),  y=xyl)) + geom_point(size=4)+ 
  labs(title="Xylem-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(xyl_rank,"Xylem_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- xyl_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Xylem ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

LRC

lrc_rank <- bc_rank[which(bc_rank$lrc*2 > bc_rank$all),]%>% arrange(desc(lrc))
lrc_rank$GeneName <- rownames(lrc_rank)

lrc_rank

A data.frame: 26 x 12

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>
WRKY9	14.59444184	4.26077798	1.584302664	0.00000000	0.000000000	0.000000000	0.000000000	0.0000000	0.000000000	7.94390354	0.80545766	AT1G68150
ARF16	13.02068521	2.30930704	0.282169631	1.17619173	0.062867295	0.000000000	0.000000000	0.0000000	0.000000000	6.66173619	2.52841331	AT4G30080
IAA1	8.42649079	1.57459699	0.158407992	0.91438152	0.000000000	0.000000000	0.000000000	0.0000000	0.000000000	4.91253603	0.86656826	AT4G14560
AIL5	6.18681707	0.34670087	0.000000000	0.16137931	0.000000000	0.003858615	0.016628469	0.0000000	0.000000000	4.49512079	1.16312903	AT5G57390
ANL2	7.72406933	1.81627086	0.678029016	0.83919527	0.081317480	0.000000000	0.000000000	0.0000000	0.000000000	3.91301596	0.39624074	AT4G00730
AT1G74840	7.03802367	0.79216261	0.262263253	0.47640862	0.379859229	0.103724502	0.078108719	0.4127525	0.015306122	3.81472680	0.70271129	AT1G74840
AT2G35910	5.89403877	0.25140897	0.086950316	0.00000000	0.000000000	0.000000000	0.000000000	0.0000000	0.000000000	3.04980461	2.50587487	AT2G35910
AT1G05805	5.62101306	0.73134990	0.147438623	0.08463056	0.000000000	0.000000000	0.000000000	0.0000000	0.000000000	2.89665376	1.76094022	AT1G05805
LBD4	4.28618263	0.57698722	0.000000000	0.32905393	0.059190925	0.007766728	0.000000000	0.0000000	0.083912430	2.85794378	0.37132761	AT1G31320
WRKY11	5.31161013	0.93106987	0.148175423	0.18886427	0.143139072	0.074074034	0.211963195	0.1748415	0.025510204	2.84133235	0.57264023	AT4G31550
AT1G21000	4.56164450	0.18957223	0.057886042	0.00000000	0.010526316	0.154082344	0.388535398	0.0000000	0.512590626	2.43269988	0.81575166	AT1G21000
GRF2	2.49907754	0.33846432	0.262743451	0.19702918	0.048134481	0.023374603	0.000000000	0.0000000	0.096837447	1.34028931	0.19220475	AT4G37740
RBR1	2.49318081	0.29070964	0.006337552	0.20516116	0.012341446	0.020100694	0.013899162	0.0000000	0.052353806	1.28988270	0.60239465	AT3G12280
AT2G42300	2.17845764	0.15520149	0.005692168	0.14585323	0.002870813	0.000000000	0.000000000	0.0000000	0.004556113	1.21114731	0.65313652	AT2G42300
CSDP1	2.24042740	0.25554447	0.087881603	0.25174182	0.031129832	0.068795108	0.081226614	0.0000000	0.004556113	1.16031167	0.29924017	AT4G36020
PYE	1.27470844	0.11917141	0.074016999	0.03755968	0.052071285	0.018826012	0.023433715	0.0000000	0.116769193	0.69282134	0.14003881	AT3G47640
NAC016	1.15907316	0.00000000	0.000000000	0.06291777	0.000000000	0.000000000	0.000000000	0.0000000	0.000000000	0.63629719	0.45985820	AT1G34180
HDG1	0.92249741	0.18877141	0.074561948	0.05149425	0.000000000	0.000000000	0.000000000	0.0000000	0.000000000	0.55821376	0.04945604	AT3G61150
HDG2	0.59744088	0.02955690	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.0000000	0.000000000	0.52222535	0.04565862	AT1G05230
AGL94	0.72766611	0.01876420	0.000000000	0.04378426	0.000000000	0.000000000	0.000000000	0.0000000	0.000000000	0.51522054	0.14989711	AT1G69540
TRFL3	0.92037699	0.06923757	0.000000000	0.12912467	0.000000000	0.000000000	0.000000000	0.0000000	0.016194401	0.51106225	0.19475810	AT1G17460
HDG12	0.65436885	0.13558859	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.0000000	0.000000000	0.38708075	0.13169951	AT1G17920
RING1	0.29964647	0.00000000	0.000000000	0.07522546	0.000000000	0.000000000	0.005030837	0.0000000	0.000000000	0.15539321	0.06399696	AT5G10380
NAC063	0.09215283	0.00000000	0.000000000	0.02376658	0.000000000	0.000000000	0.000000000	0.0000000	0.000000000	0.05143864	0.01694761	AT3G55210
BEH1	0.07537649	0.00000000	0.000000000	0.00000000	0.002870813	0.000000000	0.000000000	0.0000000	0.000000000	0.05100034	0.02150534	AT3G50750
AT4G11400	0.02217396	0.00000000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	0.0000000	0.000000000	0.02217396	0.00000000	AT4G11400

options(repr.plot.width=6, repr.plot.height=4)
ggplot(lrc_rank[1:10,], aes(x=reorder(GeneName, lrc, decreasing = FALSE),  y=lrc)) + geom_point(size=4)+ 
  labs(title="LRC-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(lrc_rank,"LRC_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- lrc_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Lateral Root Cap ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Columella

col_rank <- bc_rank[which(bc_rank$col*2 > bc_rank$all),]%>% arrange(desc(col))
col_rank$GeneName <- rownames(col_rank)

col_rank

A data.frame: 47 x 13

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<chr>
FBH4	9.21690577	0.08544729	0.015616942	0.311309772	0.041168776	0.000000000	0.000000000	0.00000000	0.000000000	3.082557208	5.68080579	AT2G42280	FBH4
LBD41	10.15628931	0.37963773	0.154816921	0.000000000	0.056226204	0.034139350	0.102615142	0.00000000	0.000000000	4.249628884	5.17922508	AT3G02550	LBD41
WRKY26	6.08956508	0.16351024	0.009279390	0.000000000	0.000000000	0.154740276	0.215891510	0.00000000	0.000000000	1.647842854	3.89830081	AT5G07100	WRKY26
WRKY33	6.83291024	0.46618916	0.205871271	0.553852838	0.294000502	0.031296264	0.041761170	0.00000000	0.000000000	1.771403980	3.46853505	AT2G38470	WRKY33
RAP2.4	6.43730169	0.22312443	0.033721735	0.213625266	0.153915889	0.127748431	0.169710654	0.31480704	0.142573262	1.730703534	3.32737144	AT1G78080	RAP2.4
AT3G57800	6.08336328	0.18974626	0.000000000	0.000000000	0.000000000	0.074188410	0.125972115	0.00000000	0.407182871	2.099200100	3.18707352	AT3G57800	AT3G57800
NAM	3.79313739	0.02291742	0.000000000	0.112133792	0.017548508	0.000000000	0.026557424	0.00000000	0.000000000	0.451476499	3.16250375	AT1G52880	NAM
AT1G76580	5.84721261	0.19579291	0.003426918	0.036145004	0.015863019	0.002283867	0.002159837	0.00000000	0.010421124	2.548629052	3.03249087	AT1G76580	AT1G76580
RAP2.1	5.77680396	0.03234374	0.000000000	0.556970110	0.008936067	0.004570844	0.017023461	1.24396318	0.000000000	0.881365198	3.03163136	AT1G46768	RAP2.1
AT2G41835	3.87965965	0.13192815	0.023170877	0.000000000	0.010526316	0.000000000	0.000000000	0.00000000	0.000000000	1.024882475	2.68915183	AT2G41835	AT2G41835
BZIP25	3.72594983	0.33605129	0.095767447	0.036387960	0.111317365	0.006809886	0.005975666	0.00000000	0.058950776	0.593971326	2.48071811	AT3G54620	BZIP25
ARF10	3.76891761	0.25108581	0.000000000	0.118337754	0.059726788	0.000000000	0.000000000	0.01976941	0.025834065	0.993883806	2.30027998	AT2G28350	ARF10
RDUF1	3.76671081	0.31386155	0.050408431	0.300161955	0.119111549	0.000000000	0.002159837	0.00000000	0.025877702	0.877671834	2.07745796	AT3G46620	RDUF1
AT3G25790	2.85056247	0.16578889	0.051120652	0.356432164	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.844790041	1.43243073	AT3G25790	AT3G25790
SCL1	2.61524374	0.14496105	0.171904099	0.075889766	0.047610224	0.262878319	0.280218106	0.00000000	0.004421769	0.232588303	1.39477210	AT1G21450	SCL1
NAC052	2.58854529	0.03733211	0.004584554	0.000000000	0.045545292	0.019973139	0.082119023	0.00000000	0.017125550	0.990821149	1.39104448	AT3G10490	NAC052
RR1	2.51617603	0.21008848	0.193746721	0.176242078	0.055379338	0.009246258	0.009702941	0.00000000	0.054767492	0.449956323	1.35704640	AT3G16857	RR1
AT5G23405	1.58562547	0.18117590	0.000000000	0.053793103	0.043515887	0.105419374	0.015084398	0.00000000	0.002199011	0.191242358	0.99319544	AT5G23405	AT5G23405
AT1G77570	1.57187534	0.09300887	0.000000000	0.000000000	0.005741627	0.118670010	0.085011664	0.00000000	0.013406625	0.283118756	0.97291779	AT1G77570	AT1G77570
TLP7	1.55209293	0.01733708	0.031176285	0.067319146	0.058993485	0.014940502	0.062609222	0.00000000	0.057371301	0.327744533	0.91460138	AT1G53320	TLP7
ATRX	1.64389257	0.10506360	0.010876555	0.054272307	0.044412242	0.068175746	0.114064503	0.00000000	0.117546164	0.229980245	0.89950122	AT1G08600	ATRX
EIN3	1.44787717	0.06541126	0.076155996	0.010404860	0.056838409	0.003446658	0.010774162	0.00000000	0.017046594	0.315519679	0.89227955	AT3G20770	EIN3
CHR11	1.71702541	0.02774173	0.029127105	0.008146673	0.037628380	0.056752901	0.070961555	0.07682890	0.156488502	0.378209665	0.87514000	AT3G06400	CHR11
NTT	1.35753953	0.06407164	0.000000000	0.107515473	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.373950787	0.81200163	AT3G57670	NTT
PC-MYB1	1.35353355	0.05645635	0.031216314	0.074043300	0.025486950	0.004567734	0.006585116	0.00000000	0.061528360	0.301661651	0.79198778	AT4G32730	PC-MYB1
AT2G44430	1.54390908	0.12554252	0.093371310	0.082599267	0.075846597	0.000000000	0.002159837	0.07442401	0.002199011	0.305627905	0.78213863	AT2G44430	AT2G44430
AT3G52250	1.50135855	0.08096882	0.018739749	0.140354817	0.050576242	0.015323379	0.051827611	0.00000000	0.096452854	0.281457852	0.76565722	AT3G52250	AT3G52250
SDG2	1.29626788	0.06100263	0.058241779	0.118773135	0.052404819	0.008217638	0.046319150	0.00000000	0.045728549	0.225443465	0.68013672	AT4G15180	SDG2
DRIP2	0.88699742	0.06037327	0.025711804	0.000000000	0.002870813	0.019964207	0.004315010	0.00000000	0.002199011	0.130957635	0.64060567	AT2G30580	DRIP2
AT5G65910	1.21587764	0.03797942	0.053339947	0.054307068	0.078933090	0.022109564	0.027478706	0.04264086	0.043911965	0.218252278	0.63692473	AT5G65910	AT5G65910
AT1G01260	0.91308189	0.06834988	0.030054645	0.010179194	0.057373150	0.019331648	0.045221122	0.00000000	0.002199011	0.183056927	0.49731631	AT1G01260	AT1G01260
PRR7	0.91532394	0.06027178	0.164146035	0.000000000	0.008612440	0.000000000	0.002159837	0.00000000	0.015853027	0.179024099	0.48525672	AT5G02810	PRR7
SNL4	0.91624830	0.05737301	0.022442995	0.091894093	0.072399865	0.029037433	0.017616834	0.00000000	0.040292127	0.121351997	0.46383995	AT1G70060	SNL4
AT3G54460	0.71523062	0.01909819	0.011438391	0.000000000	0.030172894	0.011823946	0.002159837	0.00000000	0.000000000	0.199529824	0.44100753	AT3G54460	AT3G54460
AT4G13040	0.64346424	0.06263079	0.043965360	0.058973848	0.011806881	0.000000000	0.000000000	0.00000000	0.000000000	0.069764026	0.39632334	AT4G13040	AT4G13040
BPC7	0.29284362	0.00000000	0.000000000	0.012272325	0.003827751	0.000000000	0.010647049	0.00000000	0.000000000	0.108388895	0.15770760	AT2G35550	BPC7
SPL11	0.27823988	0.00000000	0.000000000	0.000000000	0.010526316	0.000000000	0.000000000	0.00000000	0.008340497	0.108965840	0.15040723	AT1G27360	SPL11
SDG29	0.22089640	0.00000000	0.015553163	0.014721440	0.028796110	0.000000000	0.005749782	0.00000000	0.000000000	0.025081703	0.13099420	AT5G53430	SDG29
EMF2	0.19763355	0.00000000	0.013205829	0.017601364	0.024431267	0.000000000	0.005392596	0.00000000	0.028097507	0.007873928	0.10103106	AT5G51230	EMF2
AT5G25790	0.17295633	0.00000000	0.000000000	0.000000000	0.004784689	0.000000000	0.000000000	0.00000000	0.000000000	0.079505712	0.08866593	AT5G25790	AT5G25790
HDG11	0.14019222	0.00000000	0.030948599	0.000000000	0.007556757	0.002360119	0.006470182	0.00000000	0.004556113	0.000000000	0.08830045	AT1G73360	HDG11
AT2G33550	0.11818869	0.00000000	0.000000000	0.000000000	0.004784689	0.000000000	0.000000000	0.00000000	0.000000000	0.026031593	0.08737241	AT2G33550	AT2G33550
NF-YC12	0.11207378	0.00000000	0.000000000	0.000000000	0.000000000	0.015459035	0.000000000	0.00000000	0.000000000	0.024809713	0.07180503	AT5G38140	NF-YC12
MYB1	0.12609163	0.00000000	0.028175665	0.000000000	0.003827751	0.004888947	0.006470182	0.00000000	0.000000000	0.018917257	0.06381183	AT3G09230	MYB1
AT4G36050	0.08286675	0.00000000	0.009335155	0.000000000	0.005741627	0.000000000	0.002159837	0.00000000	0.000000000	0.007105468	0.05852467	AT4G36050	AT4G36050
AT1G18560	0.05197159	0.00000000	0.000000000	0.000000000	0.002870813	0.002528828	0.002159837	0.00000000	0.000000000	0.004621896	0.03979022	AT1G18560	AT1G18560
RR14	0.01474688	0.00000000	0.000000000	0.000000000	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.01474688	AT2G01760	RR14

options(repr.plot.width=6, repr.plot.height=4)
ggplot(col_rank[1:10,], aes(x=reorder(GeneName, col, decreasing = FALSE),  y=col)) + geom_point(size=4)+ 
  labs(title="Columella-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(col_rank,"Columella_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- col_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Columella ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Add tissue level

bc_rank <- bc_rank %>% mutate(ground=cor+end, epi=atri+tri, stele=per+pro+xyl+phl, epilrc=atri+tri+lrc, rc=lrc+col)

head(bc_rank)

A data.frame: 6 x 17

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	ground	epi	stele	epilrc	rc
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
BZIP9	31.71447	0.000000	0.000000	0.000000000	0.006698565	8.04635576	11.94430365	0.03622764	11.6808854	0.000000	0.000000	AT5G24800	0.006698565	0.000000	31.70777241	0.00000	0.00000
AT3G43430	29.50134	0.000000	0.000000	0.005375774	0.006698565	8.92414305	10.03369110	3.50786160	7.0235724	0.000000	0.000000	AT3G43430	0.012074338	0.000000	29.48926813	0.00000	0.00000
HAT7	32.32415	7.054065	5.626399	8.996598123	4.910315271	0.02122024	0.00000000	0.00000000	0.0000000	4.433060	1.282490	AT5G15150	13.906913394	12.680464	0.02122024	17.11352	5.71555
PLT1	22.94342	3.573806	0.000000	2.592117793	1.958605076	0.00000000	0.00000000	0.00000000	0.0000000	9.283443	5.535445	AT3G20840	4.550722869	3.573806	0.00000000	12.85725	14.81889
MYB36	19.51309	0.000000	0.000000	3.965572845	11.648226873	3.89929436	0.00000000	0.00000000	0.0000000	0.000000	0.000000	AT5G57620	15.613799719	0.000000	3.89929436	0.00000	0.00000
GATA2	30.67747	7.853413	5.533161	2.445113736	1.382593066	0.50181676	0.08967606	0.05936252	0.4627788	10.404863	1.944695	AT2G45050	3.827706802	13.386574	1.11363412	23.79144	12.34956

Ground Tissue

ground_rank <- bc_rank[which(bc_rank$ground*2 > bc_rank$all),]%>% arrange(desc(ground))
ground_rank <- ground_rank[-c(match(rownames(cor_rank), rownames(ground_rank)),match(rownames(end_rank), rownames(ground_rank))),]
ground_rank$GeneName <- rownames(ground_rank)

ground_rank

A data.frame: 35 x 18

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	ground	epi	stele	epilrc	rc	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>
MYB12	24.9903130	0.24110551	0.155912849	10.11052553	5.93936241	1.060157731	1.112878224	4.32086271	1.413089539	0.019414381	0.617004128	AT2G47460	16.04988794	0.39701836	7.90698820	0.416432743	0.636418509	MYB12
MYB3	13.9699937	0.60984641	0.022671060	3.85248356	6.57827101	1.638384163	0.167072786	0.70609703	0.132993197	0.201219738	0.060954763	AT1G22640	10.43075457	0.63251747	2.64454718	0.833737212	0.262174501	MYB3
ARR3	11.3866183	0.31216200	0.052917778	4.72919423	3.40853435	0.619778245	0.725112144	0.00000000	0.624981197	0.241675809	0.672262527	AT1G59940	8.13772857	0.36507978	1.96987159	0.606755587	0.913938336	ARR3
AN3	9.2949905	0.76003007	0.067984101	2.89514322	3.03261764	2.092481149	0.087673905	0.00000000	0.050983200	0.215306608	0.092770587	AT5G28640	5.92776086	0.82801417	2.23113825	1.043320778	0.308077196	AN3
AtHB23	9.3641950	0.56403507	0.266474042	3.30633079	1.68667744	1.367882112	1.109416779	0.17487407	0.743953144	0.063654104	0.080897453	AT1G26960	4.99300823	0.83050911	3.39612610	0.894163215	0.144551557	AtHB23
GATA16	8.8033220	0.16763151	0.000000000	3.04103843	1.81470603	1.924130832	0.247701641	0.54901066	0.041496599	0.251383147	0.766223174	AT5G49300	4.85574446	0.16763151	2.76233974	0.419014658	1.017606321	GATA16
ULT1	7.4525749	0.28099737	0.000000000	2.55419627	2.08284711	1.310984580	0.164780230	0.10545857	0.015100330	0.325720857	0.612489631	AT4G28190	4.63704338	0.28099737	1.59632371	0.606718228	0.938210488	ULT1
HSFB4	5.0981755	0.10342921	0.000000000	2.13965901	2.15503629	0.357389408	0.174115559	0.00000000	0.145918367	0.000000000	0.022627675	AT1G46264	4.29469530	0.10342921	0.67742333	0.103429215	0.022627675	HSFB4
AT1G62975	5.8694822	1.65547109	0.154284913	2.02293197	1.40170744	0.081426014	0.083870434	0.13113569	0.302474051	0.036180633	0.000000000	AT1G62975	3.42463941	1.80975600	0.59890619	1.845936635	0.036180633	AT1G62975
AT5G57150	5.3562457	0.11867220	0.000000000	0.85827788	2.53162448	0.516855553	0.461826231	0.00000000	0.793736670	0.041566157	0.033686545	AT5G57150	3.38990237	0.11867220	1.77241845	0.160238360	0.075252702	AT5G57150
AT3G24120	3.9663789	1.10825268	0.000000000	1.64426771	0.91219490	0.167949363	0.094099703	0.00000000	0.015947693	0.008485975	0.015180823	AT3G24120	2.55646261	1.10825268	0.27799676	1.116738657	0.023666799	AT3G24120
HB5	4.0171237	0.04024887	0.000000000	1.66474312	0.87650434	0.390854613	0.527293806	0.00000000	0.512384345	0.000000000	0.005094571	AT5G65310	2.54124745	0.04024887	1.43053276	0.040248875	0.005094571	HB5
AT1G63100	3.7848777	0.48255072	0.021936403	1.63220353	0.63618954	0.066889926	0.000000000	0.00000000	0.000000000	0.945107625	0.000000000	AT1G63100	2.26839307	0.50448712	0.06688993	1.449594748	0.945107625	AT1G63100
SCL3	4.1297526	0.00000000	0.000000000	0.45895004	1.76000289	0.342801212	0.128740187	0.00000000	0.000000000	0.109564975	1.329693279	AT1G50420	2.21895293	0.00000000	0.47154140	0.109564975	1.439258254	SCL3
AT3G61420	3.4846153	1.15104176	0.229900919	1.42523024	0.59310177	0.021501844	0.014822332	0.00000000	0.000000000	0.013960953	0.035055509	AT3G61420	2.01833201	1.38094268	0.03632418	1.394903634	0.049016462	AT3G61420
MYB34	3.1675221	0.00000000	0.000000000	0.80763535	0.83879023	0.586069172	0.625573288	0.00000000	0.237965008	0.071489030	0.000000000	AT5G60890	1.64642558	0.00000000	1.44960747	0.071489030	0.071489030	MYB34
AT4G36860	2.7698259	0.03720009	0.000000000	0.54737266	0.96540492	0.086430939	0.048198441	0.45036560	0.266761683	0.026715780	0.341375799	AT4G36860	1.51277757	0.03720009	0.85175666	0.063915866	0.368091578	AT4G36860
COL4	2.1269186	0.08240666	0.006853837	0.99066948	0.37962992	0.067872114	0.040070995	0.00000000	0.000000000	0.076338855	0.483076741	AT5G24930	1.37029940	0.08926050	0.10794311	0.165599353	0.559415595	COL4
OFP12	2.1971041	0.70711487	0.114515301	0.90671943	0.37176323	0.000000000	0.000000000	0.00000000	0.000000000	0.079667654	0.017323618	AT1G05420	1.27848266	0.82163017	0.00000000	0.901297828	0.096991272	OFP12
AT4G28030	2.1177644	0.28074011	0.559077780	1.02600318	0.20719745	0.000000000	0.000000000	0.00000000	0.000000000	0.008047641	0.036698207	AT4G28030	1.23320063	0.83981789	0.00000000	0.847865534	0.044745847	AT4G28030
HB6	2.0780562	0.02771770	0.057032002	0.70289373	0.42376316	0.087077802	0.087424448	0.00000000	0.091004792	0.199154386	0.401988172	AT2G22430	1.12665689	0.08474971	0.26550704	0.283904093	0.601142558	HB6
3xHMG-box1	1.6336905	0.13488663	0.111669417	0.69006889	0.34764546	0.102381436	0.000000000	0.00000000	0.000000000	0.212967495	0.034071135	AT4G11080	1.03771436	0.24655604	0.10238144	0.459523539	0.247038630	3xHMG-box1
LCL1	1.6990622	0.01173029	0.000000000	0.43878391	0.50789186	0.162660742	0.366807281	0.04833887	0.018224451	0.000000000	0.144624779	AT5G02840	0.94667577	0.01173029	0.59603134	0.011730290	0.144624779	LCL1
ZFN1	1.5993599	0.00000000	0.000000000	0.62544006	0.25317652	0.041587771	0.066446955	0.00000000	0.052084732	0.127920780	0.432703120	AT3G02830	0.87861659	0.00000000	0.16011946	0.127920780	0.560623900	ZFN1
AT5G58900	1.3231690	0.16242466	0.127262042	0.59026020	0.15109436	0.003446658	0.007547768	0.00000000	0.207962204	0.006661683	0.066509384	AT5G58900	0.74135456	0.28968670	0.21895663	0.296348385	0.073171067	AT5G58900
MYB14	1.1552817	0.18165412	0.000000000	0.47417819	0.15312211	0.083405713	0.111064144	0.09899904	0.000000000	0.000000000	0.052858356	AT2G31180	0.62730030	0.18165412	0.29346890	0.181654123	0.052858356	MYB14
AT3G61180	1.0406172	0.04931677	0.081243024	0.37054951	0.25101662	0.068276349	0.120640298	0.03979186	0.051257569	0.000000000	0.008525249	AT3G61180	0.62156613	0.13055980	0.27996607	0.130559796	0.008525249	AT3G61180
GATA27	0.7253749	0.00000000	0.080171998	0.18313589	0.30006535	0.040923176	0.057736578	0.00000000	0.030908749	0.000000000	0.032433154	AT5G47140	0.48320124	0.08017200	0.12956850	0.080171998	0.032433154	GATA27
TGA3	0.6985618	0.08372744	0.033861981	0.32226839	0.04694648	0.000000000	0.000000000	0.00000000	0.075022740	0.010980192	0.125754625	AT1G22070	0.36921487	0.11758942	0.07502274	0.128569609	0.136734817	TGA3
BIM3	0.5910135	0.02465899	0.051597420	0.22624226	0.12588493	0.060461807	0.024197811	0.00000000	0.000000000	0.037090713	0.040879601	AT5G38860	0.35212719	0.07625641	0.08465962	0.113347120	0.077970314	BIM3
AT5G43530	0.6133244	0.14625420	0.119769012	0.14882405	0.15912259	0.039354577	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT5G43530	0.30794664	0.26602321	0.03935458	0.266023208	0.000000000	AT5G43530
NSI	0.4786957	0.06990425	0.079664129	0.12755403	0.12785536	0.023029209	0.024790483	0.00000000	0.007935578	0.000000000	0.017962618	AT1G32070	0.25540939	0.14956838	0.05575527	0.149568376	0.017962618	NSI
EPR1	0.4281435	0.00000000	0.000000000	0.11887608	0.13018644	0.020861604	0.017112362	0.08237797	0.000000000	0.007873928	0.050855067	AT1G18330	0.24906252	0.00000000	0.12035194	0.007873928	0.058728995	EPR1
NF-YA2	0.1928269	0.00000000	0.000000000	0.03649635	0.06719679	0.033387370	0.055746427	0.00000000	0.000000000	0.000000000	0.000000000	AT3G05690	0.10369314	0.00000000	0.08913380	0.000000000	0.000000000	NF-YA2
SUVR5	0.1191541	0.00000000	0.000000000	0.01222965	0.05468312	0.006975001	0.003237423	0.00000000	0.000000000	0.000000000	0.042028910	AT2G23740	0.06691277	0.00000000	0.01021242	0.000000000	0.042028910	SUVR5

options(repr.plot.width=6, repr.plot.height=4)
ggplot(ground_rank[1:10,], aes(x=reorder(GeneName, ground, decreasing = FALSE),  y=ground)) + geom_point(size=4)+ 
  labs(title="Ground Tissue-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(ground_rank,"Ground_Tissue_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- ground_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Ground Tissue ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Epidermis

epi_rank <- bc_rank[which(bc_rank$epi*2 > bc_rank$all),]%>% arrange(desc(epi))
epi_rank <- epi_rank[-c(match(rownames(atri_rank), rownames(epi_rank)),match(rownames(tri_rank), rownames(epi_rank))),]
epi_rank$GeneName <- rownames(epi_rank)

epi_rank

A data.frame: 56 x 18

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	ground	epi	stele	epilrc	rc	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>
KDR	15.63637057	5.50850153	5.72027010	0.836563394	0.427781125	0.095169474	0.000000000	0.00000000	0.000000000	2.997848939	0.050236005	AT1G26945	1.264344519	11.22877163	0.095169474	14.22662057	3.048084944	KDR
MYB23	17.41583495	7.91650475	3.14725289	1.032895899	0.214463455	0.000000000	0.000000000	0.00000000	0.000000000	5.011080445	0.093637514	AT5G40330	1.247359354	11.06375763	0.000000000	16.07483808	5.104717959	MYB23
ATS	17.65096816	5.17751248	4.08309220	1.121933126	0.820489845	0.047344564	0.000000000	0.00000000	0.000000000	6.028496767	0.372099180	AT5G42630	1.942422971	9.26060468	0.047344564	15.28910145	6.400595947	ATS
ARR5	9.75929636	4.35875276	3.80574116	0.238071851	0.192309442	0.014950033	0.167717646	0.00000000	0.168027211	0.489912867	0.323813394	AT3G48100	0.430381293	8.16449392	0.350694890	8.65440679	0.813726262	ARR5
ATMYC1	9.80811233	3.70117609	4.34305282	1.095044031	0.562222780	0.106616602	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT4G00480	1.657266811	8.04422891	0.106616602	8.04422891	0.000000000	ATMYC1
ATHB13	13.41075230	3.89415528	3.69945422	1.352345733	1.251681506	0.670197321	0.352521349	0.00000000	0.318160955	1.829539903	0.042696030	AT1G69780	2.604027239	7.59360950	1.340879625	9.42314941	1.872235933	ATHB13
RSL1	7.39076516	3.28965109	3.21992453	0.418898344	0.457088570	0.005202625	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT5G37800	0.875986914	6.50957562	0.005202625	6.50957562	0.000000000	RSL1
NFL	10.88309411	3.63496826	2.68247443	1.268092591	1.101495379	0.000000000	0.000000000	0.00000000	0.000000000	1.922524399	0.273539040	AT5G65640	2.369587970	6.31744270	0.000000000	8.23996710	2.196063439	NFL
GATA12	12.17640944	1.81910371	4.49478753	0.108824050	0.000000000	0.261791106	0.306764326	4.28527569	0.407810048	0.400064128	0.091988854	AT5G25830	0.108824050	6.31389124	5.261641170	6.71395537	0.492052982	GATA12
EGL3	8.38759348	3.39927159	2.39422405	1.477131504	1.008861335	0.108105000	0.000000000	0.00000000	0.000000000	0.000000000	0.000000000	AT1G63650	2.485992838	5.79349564	0.108105000	5.79349564	0.000000000	EGL3
TRY	8.88411246	4.41784935	0.63863608	1.319968497	0.752182303	0.088667884	0.000000000	0.00000000	0.008954135	1.657854209	0.000000000	AT5G53200	2.072150800	5.05648544	0.097622019	6.71433965	1.657854209	TRY
WRKY72	4.01793743	1.67680162	1.16332672	0.000000000	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.826636469	0.351172610	AT5G15130	0.000000000	2.84012835	0.000000000	3.66676482	1.177809079	WRKY72
WRKY27	5.21805179	2.56543911	0.17533706	0.063625111	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	2.351739160	0.061911349	AT5G52830	0.063625111	2.74077617	0.000000000	5.09251533	2.413650509	WRKY27
OFP13	3.05215018	0.83208007	1.48386853	0.432309283	0.151962828	0.000000000	0.000000000	0.00000000	0.000000000	0.124689823	0.027239642	AT5G04820	0.584272111	2.31594860	0.000000000	2.44063842	0.151929464	OFP13
AT5G06550	4.43491076	1.11658778	1.15662566	0.482720262	1.101944160	0.394482823	0.000000000	0.00000000	0.000000000	0.153388934	0.029161143	AT5G06550	1.584664421	2.27321344	0.394482823	2.42660237	0.182550077	AT5G06550
MYB50	4.16385948	1.90349196	0.26344782	0.063898110	0.000000000	0.000000000	0.015123526	1.78048309	0.137414966	0.000000000	0.000000000	AT1G57560	0.063898110	2.16693978	1.933021586	2.16693978	0.000000000	MYB50
AT5G61590	4.05696819	1.76770185	0.36349147	0.374509616	0.308601276	0.616218495	0.349628095	0.00000000	0.267143654	0.009673731	0.000000000	AT5G61590	0.683110892	2.13119332	1.232990245	2.14086705	0.009673731	AT5G61590
AT4G26810	2.94057549	1.28035975	0.65485347	0.142988506	0.351305180	0.364657711	0.129460206	0.00000000	0.000000000	0.016950663	0.000000000	AT4G26810	0.494293685	1.93521322	0.494117917	1.95216388	0.016950663	AT4G26810
AT1G68670	3.37214158	0.60801261	1.32361005	0.000000000	0.057710857	0.002360119	0.000000000	0.00000000	0.238223060	0.680876039	0.461348839	AT1G68670	0.057710857	1.93162266	0.240583180	2.61249870	1.142224878	AT1G68670
HFR1	2.46317247	1.01909707	0.76336647	0.506799807	0.167100660	0.000000000	0.000000000	0.00000000	0.000000000	0.000000000	0.006808466	AT1G02340	0.673900468	1.78246354	0.000000000	1.78246354	0.006808466	HFR1
ZF1	2.57285669	0.44103254	1.12693894	0.000000000	0.000000000	0.000000000	0.000000000	0.65073785	0.000000000	0.333512717	0.020634643	AT5G67450	0.000000000	1.56797148	0.650737852	1.90148420	0.354147360	ZF1
AT2G18670	1.70001397	0.82463375	0.34650347	0.266462317	0.021644938	0.000000000	0.000000000	0.00000000	0.000000000	0.139943867	0.100825634	AT2G18670	0.288107255	1.17113722	0.000000000	1.31108108	0.240769500	AT2G18670
NAC053	2.29336396	0.74177277	0.42063908	0.100847469	0.036126970	0.000000000	0.000000000	0.16401253	0.004556113	0.414178361	0.411230667	AT3G10500	0.136974439	1.16241185	0.168568646	1.57659021	0.825409028	NAC053
AT5G13780	1.99613918	0.44642879	0.60102711	0.069849691	0.066129085	0.353646923	0.230056062	0.11311248	0.053463795	0.025717884	0.036707360	AT5G13780	0.135978775	1.04745590	0.750279257	1.07317379	0.062425244	AT5G13780
HB16	1.87511366	0.20570482	0.77338062	0.567630331	0.215291113	0.023105528	0.017271060	0.00000000	0.004556113	0.010311318	0.057862761	AT4G40060	0.782921444	0.97908544	0.044932700	0.98939676	0.068174080	HB16
MED6	1.30860388	0.39651870	0.57798730	0.043900115	0.091479439	0.069739039	0.023713302	0.00000000	0.038811917	0.031536178	0.034917888	AT3G21350	0.135379554	0.97450600	0.132264259	1.00604218	0.066454065	MED6
DEL3	1.69378156	0.46389094	0.49540214	0.084526967	0.085062184	0.096845365	0.000000000	0.27360170	0.000000000	0.175128009	0.019324260	AT3G01330	0.169589151	0.95929307	0.370447068	1.13442108	0.194452269	DEL3
LOL2	1.43289572	0.57018972	0.38671468	0.089761273	0.051756184	0.051954907	0.000000000	0.00000000	0.012280090	0.180933472	0.089305405	AT4G21610	0.141517457	0.95690439	0.064234996	1.13783787	0.270238877	LOL2
ERF104	1.55884430	0.48371345	0.37942088	0.048994123	0.098413189	0.228545929	0.157862770	0.00000000	0.000000000	0.118491503	0.043402452	AT5G61600	0.147407312	0.86313434	0.386408698	0.98162584	0.161893955	ERF104
ORC1B	1.40586762	0.48452830	0.37131807	0.057648099	0.070220036	0.177429140	0.029184181	0.13505298	0.020068027	0.055870217	0.004548579	AT4G12620	0.127868135	0.85584636	0.361734327	0.91171658	0.060418796	ORC1B
NF-YC10	1.13287856	0.34202064	0.40452676	0.081250553	0.165004904	0.023468601	0.000000000	0.00000000	0.004556113	0.042649464	0.069401524	AT1G07980	0.246255457	0.74654740	0.028024713	0.78919686	0.112050987	NF-YC10
LSD1	1.22257045	0.19401863	0.48263270	0.013510450	0.044027108	0.009240825	0.029977546	0.00000000	0.044529545	0.124384092	0.280249549	AT4G20380	0.057537558	0.67665134	0.083747916	0.80103543	0.404633641	LSD1
AT1G61990	1.09627751	0.29892990	0.37235986	0.154341291	0.131479722	0.117897316	0.005392596	0.00000000	0.000000000	0.000000000	0.015876819	AT1G61990	0.285821013	0.67128976	0.123289912	0.67128976	0.015876819	AT1G61990
MBD6	0.99939397	0.19527047	0.45850882	0.092873563	0.090353997	0.100956187	0.000000000	0.02308060	0.002199011	0.007873928	0.028277393	AT5G59380	0.183227560	0.65377929	0.126235796	0.66165322	0.036151321	MBD6
AT3G55080	1.11758451	0.36882271	0.20671774	0.194376658	0.129178625	0.094622245	0.000000000	0.00000000	0.000000000	0.110047964	0.013818567	AT3G55080	0.323555283	0.57554045	0.094622245	0.68558841	0.123866532	AT3G55080
RSZ22a	1.06330763	0.27118004	0.27139636	0.000000000	0.130295883	0.187715841	0.000000000	0.00000000	0.052750487	0.106173037	0.043795981	AT2G24590	0.130295883	0.54257640	0.240466328	0.64874944	0.149969018	RSZ22a
AT3G05760	0.82701523	0.14552572	0.34630135	0.000000000	0.035799802	0.062511018	0.062391145	0.05375433	0.058549157	0.008987638	0.053195073	AT3G05760	0.035799802	0.49182708	0.237205646	0.50081471	0.062182710	AT3G05760
WRKY3	0.83517100	0.23339726	0.25165891	0.000000000	0.003827751	0.000000000	0.022825760	0.00000000	0.030647791	0.118328657	0.174484864	AT2G03340	0.003827751	0.48505618	0.053473551	0.60338484	0.292813520	WRKY3
HB23	0.71949770	0.16367781	0.31742843	0.000000000	0.050938646	0.000000000	0.021180464	0.05784531	0.108427034	0.000000000	0.000000000	AT5G39760	0.050938646	0.48110624	0.187452813	0.48110624	0.000000000	HB23
ATMAK3	0.78119836	0.29346665	0.15864409	0.012275158	0.075267666	0.049911134	0.034977491	0.00000000	0.004556113	0.140329779	0.011770281	AT2G38130	0.087542823	0.45211074	0.089444738	0.59244052	0.152100060	ATMAK3
MBD4	0.82192013	0.24648883	0.17721168	0.008146673	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.218614167	0.171458782	AT3G63030	0.008146673	0.42370051	0.000000000	0.64231467	0.390072949	MBD4
AT5G28300	0.63657257	0.26760001	0.15325679	0.148011863	0.005741627	0.000000000	0.000000000	0.00000000	0.032480199	0.029482081	0.000000000	AT5G28300	0.153753490	0.42085680	0.032480199	0.45033888	0.029482081	AT5G28300
AT1G76110	0.73035755	0.11297715	0.29241386	0.078302387	0.173964740	0.031439010	0.009497407	0.00000000	0.000000000	0.031763003	0.000000000	AT1G76110	0.252267127	0.40539101	0.040936417	0.43715401	0.031763003	AT1G76110
NPR1	0.70242534	0.13965071	0.24574478	0.045110396	0.046378011	0.004567734	0.005749782	0.00000000	0.000000000	0.058166371	0.157057553	AT1G64280	0.091488407	0.38539550	0.010317516	0.44356187	0.215223923	NPR1
RAD54	0.31770260	0.14839746	0.12483589	0.000000000	0.000000000	0.007766728	0.000000000	0.00000000	0.000000000	0.025101594	0.011600918	AT3G19210	0.000000000	0.27323336	0.007766728	0.29833495	0.036702511	RAD54
KAPP	0.38198942	0.08954099	0.17377527	0.015568843	0.029638329	0.002360119	0.006470182	0.00000000	0.010133380	0.000000000	0.054502311	AT5G19280	0.045207172	0.26331625	0.018963682	0.26331625	0.054502311	KAPP
BEH4	0.47379993	0.19259354	0.06072757	0.105894003	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.068453544	0.046131270	AT1G78700	0.105894003	0.25332111	0.000000000	0.32177465	0.114584814	BEH4
SDG40	0.50071456	0.06691592	0.18389081	0.038337754	0.006698565	0.128588147	0.002159837	0.03629277	0.000000000	0.017462969	0.020367788	AT5G17240	0.045036319	0.25080672	0.167040758	0.26826969	0.037830757	SDG40
PUX2	0.45500267	0.09083881	0.15158815	0.038899216	0.076819385	0.003446658	0.011747163	0.00000000	0.006914603	0.027064963	0.047683717	AT2G01650	0.115718600	0.24242697	0.022108423	0.26949193	0.074748680	PUX2
CHR8	0.36472498	0.08129574	0.15186503	0.000000000	0.024968358	0.015266569	0.020145951	0.00000000	0.008800378	0.000000000	0.062382958	AT2G18760	0.024968358	0.23316077	0.044212898	0.23316077	0.062382958	CHR8
AT5G26749	0.28158564	0.11639400	0.05797125	0.000000000	0.058357436	0.026133672	0.016993675	0.00000000	0.000000000	0.005735605	0.000000000	AT5G26749	0.058357436	0.17436525	0.043127347	0.18010086	0.005735605	AT5G26749
PRR9	0.25641187	0.04184015	0.09924032	0.048311229	0.006698565	0.000000000	0.004425279	0.00000000	0.000000000	0.030979971	0.024916367	AT2G46790	0.055009794	0.14108046	0.004425279	0.17206043	0.055896338	PRR9
EMB2219	0.18136003	0.07410954	0.03446078	0.000000000	0.029574962	0.016040059	0.007074285	0.00000000	0.000000000	0.000000000	0.020100406	AT2G21710	0.029574962	0.10857031	0.023114344	0.10857031	0.020100406	EMB2219
AT2G46040	0.16369601	0.01823264	0.06883869	0.006114153	0.025486950	0.007766728	0.000000000	0.00000000	0.004556113	0.004365742	0.028335003	AT2G46040	0.031601103	0.08707132	0.012322840	0.09143707	0.032700745	AT2G46040
AT3G51180	0.11114410	0.04024887	0.02705086	0.000000000	0.029753047	0.002360119	0.011731201	0.00000000	0.000000000	0.000000000	0.000000000	AT3G51180	0.029753047	0.06729974	0.014091321	0.06729974	0.000000000	AT3G51180
AT1G61960	0.07369076	0.01928682	0.03022349	0.000000000	0.002870813	0.010135432	0.000000000	0.00000000	0.000000000	0.004365742	0.006808466	AT1G61960	0.002870813	0.04951031	0.010135432	0.05387605	0.011174208	AT1G61960

options(repr.plot.width=6, repr.plot.height=4)
ggplot(epi_rank[1:10,], aes(x=reorder(GeneName, epi, decreasing = FALSE),  y=epi)) + geom_point(size=4)+ 
  labs(title="Epidermis-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(epi_rank,"Epidermis_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- epi_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Epidermis ", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Epidermis + LRC

epilrc_rank <- bc_rank[which(bc_rank$epilrc*2 > bc_rank$all),]%>% arrange(desc(epilrc))
epilrc_rank <- epilrc_rank[-c(match(rownames(atri_rank), rownames(epilrc_rank)),match(rownames(tri_rank), rownames(epilrc_rank)),match(rownames(lrc_rank), rownames(epilrc_rank))),]
epilrc_rank$GeneName <- rownames(epilrc_rank)

epilrc_rank

A data.frame: 195 x 18

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	ground	epi	stele	epilrc	rc	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>
GATA2	30.677473	7.8534131	5.533161034	2.4451137	1.38259307	0.50181676	0.08967606	0.05936252	0.462778785	10.4048628	1.94469509	AT2G45050	3.8277068	13.3865741	1.11363412	23.791437	12.3495579	GATA2
HAT7	32.324147	7.0540648	5.626398815	8.9965981	4.91031527	0.02122024	0.00000000	0.00000000	0.000000000	4.4330601	1.28248975	AT5G15150	13.9069134	12.6804636	0.02122024	17.113524	5.7155498	HAT7
MYB23	17.415835	7.9165047	3.147252887	1.0328959	0.21446346	0.00000000	0.00000000	0.00000000	0.000000000	5.0110804	0.09363751	AT5G40330	1.2473594	11.0637576	0.00000000	16.074838	5.1047180	MYB23
CRF2	28.881788	4.5879796	2.175573608	2.2096198	1.70007169	2.54559752	0.39109962	0.15510466	2.724359039	8.9251989	3.46718327	AT4G23750	3.9096915	6.7635533	5.81616083	15.688752	12.3923822	CRF2
ATS	17.650968	5.1775125	4.083092197	1.1219331	0.82048984	0.04734456	0.00000000	0.00000000	0.000000000	6.0284968	0.37209918	AT5G42630	1.9424230	9.2606047	0.04734456	15.289101	6.4005959	ATS
BT2	21.716559	4.2550728	1.229291484	0.2692809	0.01052632	0.00000000	0.00000000	0.00000000	0.000000000	9.1867705	6.76561708	AT3G48360	0.2798072	5.4843643	0.00000000	14.671135	15.9523876	BT2
NAI1	25.350401	3.3551095	0.367456996	1.2531594	0.05930131	0.00000000	0.00000000	0.00000000	0.000000000	10.7691324	9.54624150	AT2G22770	1.3124607	3.7225665	0.00000000	14.491699	20.3153739	NAI1
CRF3	20.564564	3.2745045	2.361877234	1.3429950	0.89743390	0.18772725	0.02324283	0.00000000	0.000000000	8.6413544	3.83542869	AT5G53290	2.2404289	5.6363817	0.21097009	14.277736	12.4767830	CRF3
KDR	15.636371	5.5085015	5.720270099	0.8365634	0.42778112	0.09516947	0.00000000	0.00000000	0.000000000	2.9978489	0.05023601	AT1G26945	1.2643445	11.2287716	0.09516947	14.226621	3.0480849	KDR
WER	20.004146	5.1075406	2.647926229	2.2659865	1.96000982	1.38211375	0.00000000	0.00000000	0.000000000	5.9792996	0.66126914	AT5G14750	4.2259963	7.7554669	1.38211375	13.734766	6.6405687	WER
PLT1	22.943416	3.5738055	0.000000000	2.5921178	1.95860508	0.00000000	0.00000000	0.00000000	0.000000000	9.2834430	5.53544462	AT3G20840	4.5507229	3.5738055	0.00000000	12.857248	14.8188876	PLT1
GATA4	18.285184	5.5129595	2.390551292	2.8503033	2.11585510	0.20661265	0.03511474	0.11214608	0.113605442	4.7034241	0.24461154	AT3G60530	4.9661584	7.9035108	0.46747892	12.606935	4.9480356	GATA4
SMB	19.125611	4.2433488	0.000000000	1.5820659	0.17768211	0.00000000	0.00000000	0.00000000	0.000000000	7.8072892	5.31522494	AT1G79580	1.7597480	4.2433488	0.00000000	12.050638	13.1225142	SMB
BRON	19.061757	2.9381675	0.903218088	1.8428956	0.95924294	0.03377985	0.00000000	0.00000000	0.000000000	7.6392464	4.74520673	AT1G75710	2.8021385	3.8413856	0.03377985	11.480632	12.3844532	BRON
TMO7	20.423556	5.3293805	2.559039942	3.1713960	0.57305235	0.74175960	0.00000000	0.00000000	0.004556113	3.3049149	4.73945683	AT1G74500	3.7444483	7.8884204	0.74631571	11.193335	8.0443717	TMO7
ERF9	15.875276	3.2978012	1.303157918	1.5582184	0.17717644	1.03249249	0.03976590	1.52275919	0.062244898	5.8737052	1.00795472	AT5G44210	1.7353948	4.6009591	2.65726248	10.474664	6.8816599	ERF9
LBD15	18.810274	1.9153651	0.007969035	0.6176195	0.00000000	0.00000000	0.00000000	2.30414675	0.000000000	8.5011616	5.46401160	AT2G40470	0.6176195	1.9233341	2.30414675	10.424496	13.9651732	LBD15
ATHB13	13.410752	3.8941553	3.699454220	1.3523457	1.25168151	0.67019732	0.35252135	0.00000000	0.318160955	1.8295399	0.04269603	AT1G69780	2.6040272	7.5936095	1.34087963	9.423149	1.8722359	ATHB13
RITF1	17.573349	3.9313725	1.652504912	2.4842532	1.92087708	1.91999519	0.00000000	0.00000000	0.000000000	3.6202586	2.04408737	AT2G12646	4.4051303	5.5838774	1.91999519	9.204136	5.6643460	RITF1
AT1G26680	17.203575	0.3927038	0.000000000	1.7351848	0.00000000	0.00000000	0.00000000	0.00000000	0.000000000	8.4961376	6.57954848	AT1G26680	1.7351848	0.3927038	0.00000000	8.888841	15.0756860	AT1G26680
NAC094	15.991661	3.5020507	0.000000000	2.1185459	1.22356383	0.00000000	0.00000000	0.00000000	0.000000000	5.2559830	3.89151757	AT5G39820	3.3421097	3.5020507	0.00000000	8.758034	9.1475006	NAC094
ARR5	9.759296	4.3587528	3.805741157	0.2380719	0.19230944	0.01495003	0.16771765	0.00000000	0.168027211	0.4899129	0.32381339	AT3G48100	0.4303813	8.1644939	0.35069489	8.654407	0.8137263	ARR5
FEZ	16.902047	3.6356756	0.000000000	2.4628387	2.07276093	0.00000000	0.00000000	0.00000000	0.000000000	4.9090076	3.82176365	AT1G26870	4.5355997	3.6356756	0.00000000	8.544683	8.7307713	FEZ
GATA17L	14.982029	2.7267364	1.720431414	1.9530114	1.39698574	2.00184546	0.10007701	0.02308060	0.000000000	3.8564422	1.20341848	AT4G16141	3.3499972	4.4471678	2.12500306	8.303610	5.0598607	GATA17L
NFL	10.883094	3.6349683	2.682474434	1.2680926	1.10149538	0.00000000	0.00000000	0.00000000	0.000000000	1.9225244	0.27353904	AT5G65640	2.3695880	6.3174427	0.00000000	8.239967	2.1960634	NFL
ATMYC1	9.808112	3.7011761	4.343052820	1.0950440	0.56222278	0.10661660	0.00000000	0.00000000	0.000000000	0.0000000	0.00000000	AT4G00480	1.6572668	8.0442289	0.10661660	8.044229	0.0000000	ATMYC1
EEL	12.245219	2.7984407	0.000000000	1.4599654	0.38018280	0.07532643	0.00000000	0.00000000	0.000000000	5.2091292	2.32217441	AT2G41070	1.8401482	2.7984407	0.07532643	8.007570	7.5313036	EEL
GATA17	12.698020	3.1544619	1.965477179	1.7138198	1.72850710	1.27124372	0.00000000	0.00000000	0.000000000	2.6483608	0.21614945	AT3G16870	3.4423269	5.1199391	1.27124372	7.768300	2.8645103	GATA17
WRKY17	11.752376	2.1393064	1.385138128	1.0072679	0.55127636	0.16125705	0.10230446	1.23415275	0.150082151	4.1620318	0.85955851	AT2G24570	1.5585443	3.5244445	1.64779641	7.686476	5.0215903	WRKY17
ZFP5	14.793854	2.2673463	0.152957279	0.0000000	0.00000000	0.07725790	0.24114298	3.43745358	0.133673469	5.0787528	3.40526939	AT1G10480	0.0000000	2.4203036	3.88952793	7.499056	8.4840222	ZFP5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
AT3G53440	0.33941772	0.04925169	0.097559758	0.000000000	0.028147841	0.000000000	0.000000000	0.00000000	0.000000000	0.110430916	0.054027516	AT3G53440	0.028147841	0.14681144	0.000000000	0.25724236	0.164458432	AT3G53440
AT3G07670	0.43754447	0.17874250	0.021748913	0.040707339	0.025843925	0.053796999	0.035114739	0.00000000	0.003359876	0.048624568	0.029605610	AT3G07670	0.066551264	0.20049141	0.092271614	0.24911598	0.078230178	AT3G07670
AT1G72030	0.44316473	0.20052069	0.000000000	0.094500442	0.000000000	0.026310804	0.000000000	0.00000000	0.000000000	0.034285772	0.087547030	AT1G72030	0.094500442	0.20052069	0.026310804	0.23480646	0.121832801	AT1G72030
AT1G60700	0.37897676	0.12119459	0.066048781	0.026065429	0.039091123	0.000000000	0.005975666	0.04612863	0.023967719	0.047057380	0.003447441	AT1G60700	0.065156552	0.18724337	0.076072011	0.23430075	0.050504821	AT1G60700
CHR8	0.36472498	0.08129574	0.151865025	0.000000000	0.024968358	0.015266569	0.020145951	0.00000000	0.008800378	0.000000000	0.062382958	AT2G18760	0.024968358	0.23316077	0.044212898	0.23316077	0.062382958	CHR8
AT3G52100	0.46382052	0.05644215	0.096780854	0.024250893	0.074519252	0.013750992	0.016993675	0.00000000	0.010316398	0.078991278	0.091775026	AT3G52100	0.098770145	0.15322300	0.041061065	0.23221428	0.170766304	AT3G52100
AT3G21810	0.39703523	0.10712582	0.046297170	0.000000000	0.005741627	0.005975486	0.011010394	0.00000000	0.034081192	0.073122846	0.113680699	AT3G21810	0.005741627	0.15342299	0.051067072	0.22654583	0.186803545	AT3G21810
TRFL7	0.43307984	0.09031958	0.017324134	0.060654288	0.026160354	0.067634125	0.002159837	0.00000000	0.000000000	0.118821956	0.050005565	AT1G06910	0.086814642	0.10764372	0.069793962	0.22646567	0.168827520	TRFL7
AT1G19490	0.41042627	0.02808975	0.006853837	0.012272325	0.003827751	0.000000000	0.000000000	0.00000000	0.000000000	0.191450585	0.167932023	AT1G19490	0.016100077	0.03494358	0.000000000	0.22639417	0.359382608	AT1G19490
AT1G77250	0.38779598	0.03085115	0.058274008	0.022148446	0.055871591	0.010655722	0.010085824	0.00000000	0.002199011	0.107642409	0.090067819	AT1G77250	0.078020037	0.08912515	0.022940557	0.19676756	0.197710228	AT1G77250
HAT3.1	0.37652484	0.03015349	0.101703625	0.000000000	0.004784689	0.028590476	0.013207632	0.03694286	0.022360760	0.062872448	0.075908860	AT3G19510	0.004784689	0.13185712	0.101101723	0.19472957	0.138781308	HAT3.1
LSMT-L	0.27575885	0.06035469	0.005184388	0.017127618	0.000000000	0.000000000	0.000000000	0.01647994	0.000000000	0.124557552	0.052054663	AT1G14030	0.017127618	0.06553908	0.016479937	0.19009663	0.176612214	LSMT-L
AT3G46950	0.35653805	0.13407905	0.039635518	0.135879752	0.000000000	0.014406072	0.007547768	0.00000000	0.000000000	0.009026657	0.015963234	AT3G46950	0.135879752	0.17371456	0.021953841	0.18274122	0.024989891	AT3G46950
AT5G26749	0.28158564	0.11639400	0.057971249	0.000000000	0.058357436	0.026133672	0.016993675	0.00000000	0.000000000	0.005735605	0.000000000	AT5G26749	0.058357436	0.17436525	0.043127347	0.18010086	0.005735605	AT5G26749
NBS1	0.26011518	0.05899116	0.059317219	0.045305040	0.000000000	0.021751007	0.000000000	0.00000000	0.000000000	0.055311277	0.019439480	AT3G02680	0.045305040	0.11830838	0.021751007	0.17361966	0.074750757	NBS1
PRR9	0.25641187	0.04184015	0.099240316	0.048311229	0.006698565	0.000000000	0.004425279	0.00000000	0.000000000	0.030979971	0.024916367	AT2G46790	0.055009794	0.14108046	0.004425279	0.17206043	0.055896338	PRR9
CPSF30	0.30067750	0.01615208	0.070264994	0.000000000	0.023109800	0.026778052	0.013523434	0.00000000	0.005558887	0.070937865	0.074352390	AT1G30460	0.023109800	0.08641707	0.045860374	0.15735494	0.145290255	CPSF30
AT2G20110	0.28193951	0.03524579	0.000000000	0.000000000	0.004784689	0.002528828	0.000000000	0.00000000	0.000000000	0.107954546	0.131425650	AT2G20110	0.004784689	0.03524579	0.002528828	0.14320034	0.239380196	AT2G20110
SUVH5	0.24427634	0.05090729	0.062404189	0.048346596	0.040713407	0.005202625	0.000000000	0.00000000	0.014875358	0.014989602	0.006837271	AT2G35160	0.089060003	0.11331148	0.020077983	0.12830108	0.021826873	SUVH5
AT5G07400	0.17234180	0.00000000	0.085120744	0.027621574	0.004784689	0.000000000	0.000000000	0.00000000	0.000000000	0.027638536	0.027176254	AT5G07400	0.032406263	0.08512074	0.000000000	0.11275928	0.054814790	AT5G07400
EMB2219	0.18136003	0.07410954	0.034460777	0.000000000	0.029574962	0.016040059	0.007074285	0.00000000	0.000000000	0.000000000	0.020100406	AT2G21710	0.029574962	0.10857031	0.023114344	0.10857031	0.020100406	EMB2219
APTX	0.14751860	0.00000000	0.071588024	0.000000000	0.003827751	0.013823899	0.003237423	0.00000000	0.000000000	0.028966382	0.026075116	AT5G01310	0.003827751	0.07158802	0.017061323	0.10055441	0.055041498	APTX
MAC5B	0.18046972	0.02975577	0.021373931	0.000000000	0.000000000	0.029413220	0.003237423	0.00000000	0.022701985	0.042172976	0.031814419	AT2G29580	0.000000000	0.05112970	0.055352629	0.09330267	0.073987395	MAC5B
AT2G46040	0.16369601	0.01823264	0.068838688	0.006114153	0.025486950	0.007766728	0.000000000	0.00000000	0.004556113	0.004365742	0.028335003	AT2G46040	0.031601103	0.08707132	0.012322840	0.09143707	0.032700745	AT2G46040
AT3G19184	0.14029450	0.01040465	0.036787635	0.000000000	0.000000000	0.007766728	0.000000000	0.00000000	0.051226834	0.034108648	0.000000000	AT3G19184	0.000000000	0.04719229	0.058993561	0.08130094	0.034108648	AT3G19184
AT3G51180	0.11114410	0.04024887	0.027050861	0.000000000	0.029753047	0.002360119	0.011731201	0.00000000	0.000000000	0.000000000	0.000000000	AT3G51180	0.029753047	0.06729974	0.014091321	0.06729974	0.000000000	AT3G51180
AT3G07260	0.10749679	0.01565861	0.000000000	0.000000000	0.033306000	0.010330830	0.000000000	0.00000000	0.000000000	0.048201359	0.000000000	AT3G07260	0.033306000	0.01565861	0.010330830	0.06385996	0.048201359	AT3G07260
TCP4	0.09707787	0.01565861	0.000000000	0.017648099	0.012440191	0.000000000	0.000000000	0.00000000	0.000000000	0.044436088	0.006894881	AT3G15030	0.030088290	0.01565861	0.000000000	0.06009469	0.051330970	TCP4
AT1G61960	0.07369076	0.01928682	0.030223489	0.000000000	0.002870813	0.010135432	0.000000000	0.00000000	0.000000000	0.004365742	0.006808466	AT1G61960	0.002870813	0.04951031	0.010135432	0.05387605	0.011174208	AT1G61960
CIA2	0.06313337	0.00000000	0.029848508	0.016616040	0.003827751	0.000000000	0.000000000	0.00000000	0.000000000	0.012841074	0.000000000	AT5G57180	0.020443791	0.02984851	0.000000000	0.04268958	0.012841074	CIA2

options(repr.plot.width=8, repr.plot.height=40)
ggplot(epilrc_rank, aes(x=reorder(GeneName, epilrc, decreasing = FALSE),  y=epilrc)) + geom_point(size=4)+ 
  labs(title="Epidermis+LRC-specific TF Prioritization",x="", y = "Combined centrality score (betweeness, out and in degree)")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(epilrc_rank,"Epidermis_LRC_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- epilrc_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Epidermis (includes LRC)", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Stele

stele_rank <- bc_rank[which(bc_rank$stele*2 > bc_rank$all),]%>% arrange(desc(stele))
stele_rank <- stele_rank[-c(match(rownames(per_rank), rownames(stele_rank)),match(rownames(pro_rank), rownames(stele_rank)),match(rownames(xyl_rank), rownames(stele_rank)),match(rownames(phl_rank), rownames(stele_rank))),]
stele_rank$GeneName <- rownames(stele_rank)

stele_rank

A data.frame: 348 x 18

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	ground	epi	stele	epilrc	rc	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>
BZIP9	31.71447	0.00000000	0.000000000	0.000000000	0.006698565	8.0463558	11.944304	0.03622764	11.680885	0.00000000	0.00000000	AT5G24800	0.006698565	0.000000000	31.70777	0.000000000	0.00000000	BZIP9
AT3G43430	29.50134	0.00000000	0.000000000	0.005375774	0.006698565	8.9241430	10.033691	3.50786160	7.023572	0.00000000	0.00000000	AT3G43430	0.012074338	0.000000000	29.48927	0.000000000	0.00000000	AT3G43430
LEP	35.57643	3.67988635	5.198969256	0.568088689	4.608115051	12.3723072	5.865853	0.21451060	2.929608	0.13909305	0.00000000	AT5G13910	5.176203740	8.878855607	21.38228	9.017948659	0.13909305	LEP
HB-8	20.22047	0.00000000	0.008662067	0.000000000	0.000000000	2.6361377	6.036853	8.18332066	3.355497	0.00000000	0.00000000	AT4G32880	0.000000000	0.008662067	20.21181	0.008662067	0.00000000	HB-8
MYB20	19.03556	0.00000000	0.000000000	0.000000000	0.000000000	5.8160656	7.091989	3.20671764	2.920785	0.00000000	0.00000000	AT1G66230	0.000000000	0.000000000	19.03556	0.000000000	0.00000000	MYB20
HAT2	32.66051	0.00000000	0.000000000	6.841854634	6.821161147	6.2848471	4.586768	2.80786121	4.953272	0.00000000	0.36474865	AT5G47370	13.663015781	0.000000000	18.63275	0.000000000	0.36474865	HAT2
AT1G61660	18.17403	0.00000000	0.000000000	0.000000000	0.003827751	5.2904445	6.741888	1.44004495	4.697828	0.00000000	0.00000000	AT1G61660	0.003827751	0.000000000	18.17021	0.000000000	0.00000000	AT1G61660
AT5G05790	17.03660	0.00000000	0.000000000	0.000000000	0.158795612	6.5991434	3.488748	0.11344400	6.676472	0.00000000	0.00000000	AT5G05790	0.158795612	0.000000000	16.87781	0.000000000	0.00000000	AT5G05790
IAA12	16.82887	0.00000000	0.000000000	0.000000000	0.000000000	1.4006059	7.169331	5.61947935	2.639450	0.00000000	0.00000000	AT1G04550	0.000000000	0.000000000	16.82887	0.000000000	0.00000000	IAA12
DAG1	16.77714	0.00000000	0.000000000	0.000000000	0.000000000	4.5627956	6.988757	1.03954406	4.186043	0.00000000	0.00000000	AT3G61850	0.000000000	0.000000000	16.77714	0.000000000	0.00000000	DAG1
AT1G29160	17.92719	0.00000000	0.000000000	0.000000000	0.000000000	3.6244354	6.498144	0.00000000	6.534672	1.15644899	0.11348907	AT1G29160	0.000000000	0.000000000	16.65725	1.156448993	1.26993807	AT1G29160
AT3G60490	16.31230	0.00000000	0.000000000	0.000000000	0.000000000	4.6810522	5.038575	0.11546813	6.477208	0.00000000	0.00000000	AT3G60490	0.000000000	0.000000000	16.31230	0.000000000	0.00000000	AT3G60490
ATAUX2-11	16.29304	0.09194796	0.000000000	0.134816302	0.015170558	0.7387878	5.228388	7.73246237	2.169390	0.09843132	0.08364182	AT5G43700	0.149986860	0.091947958	15.86903	0.190379281	0.18207314	ATAUX2-11
GBF6	15.90920	0.00000000	0.000000000	0.030692191	0.822865104	2.5528700	4.057993	2.52959924	5.874417	0.00000000	0.04076601	AT4G34590	0.853557295	0.000000000	15.01488	0.000000000	0.04076601	GBF6
MYB43	14.25244	0.00000000	0.000000000	0.000000000	0.083918310	4.5182255	6.614451	0.33532323	2.700519	0.00000000	0.00000000	AT5G16600	0.083918310	0.000000000	14.16852	0.000000000	0.00000000	MYB43
AT4G29100	14.22914	0.00000000	0.000000000	0.000000000	0.186437105	7.0551232	4.597310	0.00000000	2.390265	0.00000000	0.00000000	AT4G29100	0.186437105	0.000000000	14.04270	0.000000000	0.00000000	AT4G29100
AT2G41130	15.89891	0.06704657	0.103382331	1.025798825	0.685158671	5.9003615	3.011974	3.91761657	1.187571	0.00000000	0.00000000	AT2G41130	1.710957495	0.170428905	14.01752	0.170428905	0.00000000	AT2G41130
GRP2B	18.32815	0.55714929	0.005692168	0.727141625	0.369605755	1.0601005	2.996230	4.87776676	4.926582	1.48514283	1.32274366	AT2G21060	1.096747380	0.562841462	13.86068	2.047984289	2.80788649	GRP2B
BZIP61	20.01519	2.40562483	0.222213629	1.436841604	1.707943482	2.9291559	3.354289	2.39724621	5.155067	0.34296047	0.06384801	AT3G58120	3.144785087	2.627838455	13.83576	2.970798924	0.40680848	BZIP61
HAT1	21.96502	1.26400857	0.082594641	1.091118254	1.008889922	4.3719031	4.638934	1.64392442	3.139757	3.62896872	1.09492642	AT4G17460	2.100008176	1.346603212	13.79452	4.975571929	4.72389514	HAT1
HB40	13.65514	0.00000000	0.088978236	0.000000000	0.000000000	0.6533290	1.369664	5.09085865	6.452311	0.00000000	0.00000000	AT4G36740	0.000000000	0.088978236	13.56616	0.088978236	0.00000000	HB40
BT1	13.72208	0.00000000	0.000000000	0.065580025	0.192824030	1.6833276	4.366075	5.24896676	2.165310	0.00000000	0.00000000	AT5G63160	0.258404056	0.000000000	13.46368	0.000000000	0.00000000	BT1
MIF1	15.27371	0.00000000	0.000000000	0.327534768	1.536133210	4.7785009	4.279324	0.00000000	4.250218	0.05521773	0.04678356	AT1G74660	1.863667979	0.000000000	13.30804	0.055217730	0.10200129	MIF1
SGR5	13.30780	0.00000000	0.000000000	0.000000000	0.014876306	1.4021166	4.667050	2.03434142	5.189417	0.00000000	0.00000000	AT2G01940	0.014876306	0.000000000	13.29292	0.000000000	0.00000000	SGR5
MYB88	13.10895	0.00000000	0.000000000	0.000000000	0.002772068	1.2191321	6.033888	3.78752809	2.065631	0.00000000	0.00000000	AT2G02820	0.002772068	0.000000000	13.10618	0.000000000	0.00000000	MYB88
AT3G11280	12.79288	0.00000000	0.000000000	0.000000000	0.028048425	4.0258395	3.021415	0.00000000	5.717573	0.00000000	0.00000000	AT3G11280	0.028048425	0.000000000	12.76483	0.000000000	0.00000000	AT3G11280
UNE12	12.85333	0.00000000	0.000000000	0.000000000	0.357978648	3.1073856	3.812328	2.05427524	3.485992	0.00000000	0.03537439	AT4G02590	0.357978648	0.000000000	12.45998	0.000000000	0.03537439	UNE12
OBP3	12.13314	0.00000000	0.000000000	0.000000000	0.000000000	2.2427787	4.954698	0.27562968	4.660031	0.00000000	0.00000000	AT3G55370	0.000000000	0.000000000	12.13314	0.000000000	0.00000000	OBP3
AT5G51780	14.69064	0.25004783	0.604400906	0.321266594	1.634404722	4.1575218	3.295748	2.08680087	2.340453	0.00000000	0.00000000	AT5G51780	1.955671316	0.854448732	11.88052	0.854448732	0.00000000	AT5G51780
AT4G24060	11.93982	0.00000000	0.000000000	0.062882405	0.370314736	3.2345887	2.822960	2.59228573	2.856792	0.00000000	0.00000000	AT4G24060	0.433197140	0.000000000	11.50663	0.000000000	0.00000000	AT4G24060
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
AT5G40710	0.26020929	0.01116550	0.000000000	0.00000000	0.058615210	0.021355507	0.065009924	0.00000000	0.104063148	0.000000000	0.000000000	AT5G40710	0.058615210	0.011165498	0.19042858	0.011165498	0.000000000	AT5G40710
AT2G19260	0.25130475	0.00000000	0.000000000	0.00000000	0.014120941	0.100731777	0.018713376	0.01978027	0.044542186	0.028499833	0.024916367	AT2G19260	0.014120941	0.000000000	0.18376761	0.028499833	0.053416200	AT2G19260
LAS	0.22382719	0.00000000	0.000000000	0.00000000	0.043810571	0.090034874	0.089981748	0.00000000	0.000000000	0.000000000	0.000000000	AT1G55580	0.043810571	0.000000000	0.18001662	0.000000000	0.000000000	LAS
VAL3	0.27926874	0.00000000	0.000000000	0.04523431	0.000000000	0.017847095	0.128852354	0.00000000	0.031292517	0.028722186	0.027320279	AT4G21550	0.045234306	0.000000000	0.17799197	0.028722186	0.056042465	VAL3
AT2G38950	0.33544261	0.00000000	0.056788452	0.00000000	0.086526802	0.057807235	0.043490422	0.00000000	0.071928191	0.002995879	0.015905624	AT2G38950	0.086526802	0.056788452	0.17322585	0.059784331	0.018901503	AT2G38950
SDG25	0.25894804	0.00000000	0.000000000	0.00000000	0.035379956	0.014167734	0.036027097	0.00000000	0.117347280	0.000000000	0.056025973	AT5G42400	0.035379956	0.000000000	0.16754211	0.000000000	0.056025973	SDG25
MBD13	0.30390691	0.00000000	0.000000000	0.05365164	0.003827751	0.078263167	0.040506647	0.00000000	0.043030201	0.031866351	0.052761156	AT5G52230	0.057479387	0.000000000	0.16180001	0.031866351	0.084627507	MBD13
HSF3	0.30421533	0.00000000	0.000000000	0.00000000	0.086451872	0.030637072	0.064599130	0.00000000	0.065830074	0.009499944	0.047197239	AT5G16820	0.086451872	0.000000000	0.16106628	0.009499944	0.056697184	HSF3
SCL21	0.28114022	0.02226175	0.033925319	0.01017919	0.000000000	0.016786360	0.004199394	0.00000000	0.139862000	0.043727902	0.010198297	AT2G04890	0.010179194	0.056187073	0.16084775	0.099914975	0.053926199	SCL21
AT5G65130	0.23730831	0.00000000	0.064772449	0.00000000	0.000000000	0.000000000	0.000000000	0.08574344	0.074334240	0.000000000	0.012458184	AT5G65130	0.000000000	0.064772449	0.16007768	0.064772449	0.012458184	AT5G65130
AT5G14370	0.17334037	0.00000000	0.000000000	0.01353632	0.002870813	0.081326309	0.042885269	0.00000000	0.032721655	0.000000000	0.000000000	AT5G14370	0.016407136	0.000000000	0.15693323	0.000000000	0.000000000	AT5G14370
AT1G19860	0.22628034	0.00000000	0.018781565	0.00000000	0.031552204	0.069462882	0.037944553	0.00000000	0.033861047	0.016512576	0.018165511	AT1G19860	0.031552204	0.018781565	0.14126848	0.035294141	0.034678087	AT1G19860
AT1G62085	0.20960622	0.02186979	0.034460777	0.00000000	0.003827751	0.102853222	0.007547768	0.01978027	0.000000000	0.000000000	0.019266650	AT1G62085	0.003827751	0.056330563	0.13018126	0.056330563	0.019266650	AT1G62085
FRS10	0.17041273	0.00000000	0.011433919	0.00000000	0.003827751	0.018565271	0.051245253	0.00000000	0.056945280	0.005735605	0.022659651	AT5G28530	0.003827751	0.011433919	0.12675580	0.017169524	0.028395256	FRS10
TGA6	0.19215983	0.00000000	0.034167033	0.00000000	0.031552204	0.043466313	0.043991676	0.00000000	0.038982605	0.000000000	0.000000000	AT3G12250	0.031552204	0.034167033	0.12644059	0.034167033	0.000000000	TGA6
AT3G02860	0.23245733	0.00000000	0.031275923	0.00000000	0.038670616	0.050736132	0.060085776	0.00000000	0.011312625	0.009663261	0.030712999	AT3G02860	0.038670616	0.031275923	0.12213453	0.040939184	0.040376259	AT3G02860
AT5G13920	0.21269532	0.00000000	0.027322404	0.01222001	0.051059473	0.018320817	0.084760563	0.00000000	0.019012050	0.000000000	0.000000000	AT5G13920	0.063279483	0.027322404	0.12209343	0.027322404	0.000000000	AT5G13920
AT1G43770	0.20676072	0.03713171	0.015616942	0.00000000	0.003827751	0.053464062	0.053156227	0.00000000	0.013406625	0.021060252	0.009097158	AT1G43770	0.003827751	0.052748648	0.12002691	0.073808900	0.030157410	AT1G43770
FRS9	0.13528633	0.00000000	0.000000000	0.00000000	0.000000000	0.031718981	0.005523898	0.02308060	0.059382051	0.015580800	0.000000000	AT4G38170	0.000000000	0.000000000	0.11970553	0.015580800	0.015580800	FRS9
NAC027	0.12413172	0.00000000	0.000000000	0.00000000	0.004784689	0.041671103	0.045320602	0.00000000	0.023200558	0.000000000	0.009154768	AT1G64105	0.004784689	0.000000000	0.11019226	0.000000000	0.009154768	NAC027
TRFL5	0.14312991	0.00000000	0.000000000	0.00000000	0.002870813	0.058813157	0.017640279	0.01975855	0.000000000	0.021390624	0.022656480	AT1G15720	0.002870813	0.000000000	0.09621199	0.021390624	0.044047105	TRFL5
bHLH11	0.18408627	0.04580234	0.000000000	0.00000000	0.000000000	0.058202728	0.019898352	0.00000000	0.017855968	0.000000000	0.042326876	AT4G36060	0.000000000	0.045802345	0.09595705	0.045802345	0.042326876	bHLH11
AT1G10610	0.14221184	0.00000000	0.000000000	0.00000000	0.002870813	0.002360119	0.048597252	0.04264086	0.002199011	0.002995879	0.040547901	AT1G10610	0.002870813	0.000000000	0.09579724	0.002995879	0.043543780	AT1G10610
BMY2	0.18348363	0.02459270	0.005692168	0.00000000	0.021140607	0.036665392	0.015084507	0.00000000	0.041931477	0.021884313	0.016492464	AT5G45300	0.021140607	0.030284867	0.09368138	0.052169180	0.038376777	BMY2
DRD1	0.09119323	0.00000000	0.000000000	0.00000000	0.003827751	0.025719936	0.014128219	0.00000000	0.038420168	0.000000000	0.009097158	AT2G16390	0.003827751	0.000000000	0.07826832	0.000000000	0.009097158	DRD1
ATE2F2	0.12868852	0.00000000	0.005184388	0.00000000	0.023153228	0.038082444	0.036911594	0.00000000	0.000000000	0.012841074	0.012515794	AT1G47870	0.023153228	0.005184388	0.07499404	0.018025461	0.025356867	ATE2F2
EMB3114	0.07913222	0.00000000	0.000000000	0.00000000	0.004784689	0.034380657	0.021674624	0.00000000	0.000000000	0.012584920	0.005707328	AT2G36000	0.004784689	0.000000000	0.05605528	0.012584920	0.018292248	EMB3114
MYB64	0.06699660	0.00000000	0.000000000	0.01017919	0.000000000	0.000000000	0.000000000	0.02968126	0.023208496	0.003927655	0.000000000	AT5G11050	0.010179194	0.000000000	0.05288975	0.003927655	0.003927655	MYB64
AT2G24680	0.04609876	0.00000000	0.021561422	0.00000000	0.000000000	0.009939028	0.014598309	0.00000000	0.000000000	0.000000000	0.000000000	AT2G24680	0.000000000	0.021561422	0.02453734	0.021561422	0.000000000	AT2G24680
SHOT1	0.02413120	0.00000000	0.000000000	0.00000000	0.004784689	0.008298713	0.011047795	0.00000000	0.000000000	0.000000000	0.000000000	AT3G60400	0.004784689	0.000000000	0.01934651	0.000000000	0.000000000	SHOT1

options(repr.plot.width=6, repr.plot.height=4)
ggplot(stele_rank[1:10,], aes(x=reorder(GeneName, stele, decreasing = FALSE),  y=stele)) + geom_point(size=4)+ 
  labs(title="Stele-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(stele_rank,"Stele_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- stele_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Stele", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))

Root Cap

rc_rank <- bc_rank[which(bc_rank$rc*2 > bc_rank$all),]%>% arrange(desc(rc))
rc_rank <- rc_rank[-c(match(rownames(lrc_rank), rownames(rc_rank)),match(rownames(col_rank), rownames(rc_rank))),]
rc_rank$GeneName <- rownames(rc_rank)

rc_rank

A data.frame: 176 x 18

	all	atri	tri	cor	end	per	pro	xyl	phl	lrc	col	GeneID	ground	epi	stele	epilrc	rc	GeneName
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<chr>
NAI1	25.350401	3.35510951	0.367456996	1.25315937	0.059301306	0.00000000	0.00000000	0.00000000	0.00000000	10.769132	9.546241	AT2G22770	1.312460674	3.72256651	0.00000000	14.491699	20.315374	NAI1
BT2	21.716559	4.25507281	1.229291484	0.26928090	0.010526316	0.00000000	0.00000000	0.00000000	0.00000000	9.186771	6.765617	AT3G48360	0.279807219	5.48436429	0.00000000	14.671135	15.952388	BT2
AT1G32700	19.481028	0.07769675	0.000000000	0.03221927	0.095772000	0.31077520	1.10140689	0.20503616	2.42733679	6.354357	8.876429	AT1G32700	0.127991275	0.07769675	4.04455503	6.432053	15.230785	AT1G32700
AT1G26680	17.203575	0.39270382	0.000000000	1.73518478	0.000000000	0.00000000	0.00000000	0.00000000	0.00000000	8.496138	6.579548	AT1G26680	1.735184777	0.39270382	0.00000000	8.888841	15.075686	AT1G26680
PLT1	22.943416	3.57380550	0.000000000	2.59211779	1.958605076	0.00000000	0.00000000	0.00000000	0.00000000	9.283443	5.535445	AT3G20840	4.550722869	3.57380550	0.00000000	12.857248	14.818888	PLT1
AT1G36060	18.204785	2.34713011	0.468806411	0.68077581	0.240522072	0.01832082	0.00000000	0.00000000	0.00000000	6.149425	8.299804	AT1G36060	0.921297881	2.81593653	0.01832082	8.965362	14.449229	AT1G36060
LBD15	18.810274	1.91536509	0.007969035	0.61761953	0.000000000	0.00000000	0.00000000	2.30414675	0.00000000	8.501162	5.464012	AT2G40470	0.617619534	1.92333413	2.30414675	10.424496	13.965173	LBD15
SMB	19.125611	4.24334879	0.000000000	1.58206588	0.177682110	0.00000000	0.00000000	0.00000000	0.00000000	7.807289	5.315225	AT1G79580	1.759747994	4.24334879	0.00000000	12.050638	13.122514	SMB
CRF3	20.564564	3.27450445	2.361877234	1.34299504	0.897433896	0.18772725	0.02324283	0.00000000	0.00000000	8.641354	3.835429	AT5G53290	2.240428936	5.63638169	0.21097009	14.277736	12.476783	CRF3
BRON	19.061757	2.93816754	0.903218088	1.84289559	0.959242938	0.03377985	0.00000000	0.00000000	0.00000000	7.639246	4.745207	AT1G75710	2.802138528	3.84138563	0.03377985	11.480632	12.384453	BRON
AXR3	17.571332	3.86085484	0.008611306	1.04979133	0.007979130	0.15702640	2.14533991	0.00000000	0.08629990	2.289563	7.965865	AT1G04250	1.057770463	3.86946614	2.38866621	6.159030	10.255429	AXR3
AIL6	11.982311	1.53425303	0.000000000	0.39819629	0.177586443	0.00000000	0.00000000	0.00000000	0.00000000	4.850310	5.021966	AT5G10510	0.575782729	1.53425303	0.00000000	6.384563	9.872276	AIL6
RAV2	12.024870	0.22948830	0.000000000	0.00000000	0.003827751	0.67746843	1.03051504	0.12209050	0.42800123	4.520432	5.013047	AT1G68840	0.003827751	0.22948830	2.25807520	4.749921	9.533479	RAV2
BIM1	13.733519	1.70321980	0.272236569	0.50585720	0.164453289	0.24330030	0.36198208	0.08576516	1.07417198	4.526706	4.795826	AT5G08130	0.670310488	1.97545637	1.76521951	6.502163	9.322532	BIM1
NAC094	15.991661	3.50205073	0.000000000	2.11854588	1.223563829	0.00000000	0.00000000	0.00000000	0.00000000	5.255983	3.891518	AT5G39820	3.342109707	3.50205073	0.00000000	8.758034	9.147501	NAC094
FEZ	16.902047	3.63567564	0.000000000	2.46283874	2.072760934	0.00000000	0.00000000	0.00000000	0.00000000	4.909008	3.821764	AT1G26870	4.535599675	3.63567564	0.00000000	8.544683	8.730771	FEZ
ZFP5	14.793854	2.26734635	0.152957279	0.00000000	0.000000000	0.07725790	0.24114298	3.43745358	0.13367347	5.078753	3.405269	AT1G10480	0.000000000	2.42030363	3.88952793	7.499056	8.484022	ZFP5
AT3G52440	9.362833	0.27888930	0.000000000	0.78371031	0.000000000	0.00000000	0.00000000	0.00000000	0.00000000	3.658878	4.641356	AT3G52440	0.783710308	0.27888930	0.00000000	3.937767	8.300234	AT3G52440
SPT	11.817388	1.64841104	0.000000000	1.24716994	0.203225366	0.21705035	0.10631062	0.30484460	0.04591837	4.455628	3.588829	AT4G36930	1.450395306	1.64841104	0.67412394	6.104039	8.044458	SPT
PLT2	13.333592	2.36200615	0.000000000	1.77150120	1.120909320	0.05700562	0.00000000	0.00000000	0.00000000	4.972074	3.050095	AT1G51190	2.892410520	2.36200615	0.05700562	7.334080	8.022169	PLT2
AGL21	10.923673	0.32407343	0.000000000	1.71661937	0.279724769	0.25158153	0.25522363	0.00000000	0.09524688	4.386963	3.614240	AT4G37940	1.996344139	0.32407343	0.60205204	4.711037	8.001203	AGL21
WRKY15	9.661925	0.56412728	0.000000000	0.68297120	0.662720653	0.02902500	0.11010806	0.00000000	0.00000000	2.846779	4.766194	AT2G23320	1.345691857	0.56412728	0.13913306	3.410906	7.612973	WRKY15
EEL	12.245219	2.79844075	0.000000000	1.45996538	0.380182803	0.07532643	0.00000000	0.00000000	0.00000000	5.209129	2.322174	AT2G41070	1.840148181	2.79844075	0.07532643	8.007570	7.531304	EEL
AT1G68920	9.480605	0.58507109	0.000000000	0.31154125	0.147887373	0.00000000	0.00000000	0.00000000	0.92726005	4.205802	3.303043	AT1G68920	0.459428621	0.58507109	0.92726005	4.790873	7.508846	AT1G68920
AT4G39780	13.782997	1.23770438	1.660505978	0.26830805	0.426178270	0.03449453	0.06075072	0.00000000	2.78283335	3.110263	4.201959	AT4G39780	0.694486323	2.89821036	2.87807861	6.008474	7.312222	AT4G39780
AT1G49475	10.288504	0.83000575	0.000000000	1.68831366	0.381413147	0.05914161	0.04252059	0.00000000	0.00000000	4.266174	3.020935	AT1G49475	2.069726808	0.83000575	0.10166221	5.096179	7.287109	AT1G49475
ATL6	9.325592	0.78001899	0.072562100	1.26275288	0.111286723	0.03839192	0.10885699	0.06269541	0.15937929	3.111895	3.617753	AT3G05200	1.374039602	0.85258109	0.36932361	3.964476	6.729648	ATL6
IAA33	10.961264	1.63762467	0.000000000	1.87717292	0.902536907	0.00000000	0.00000000	0.00000000	0.00000000	3.555443	2.988487	AT5G57420	2.779709822	1.63762467	0.00000000	5.193067	6.543930	IAA33
HSFA7A	8.110707	0.19182285	0.003426918	0.10888833	0.099361398	0.15345988	0.21868553	0.07568389	0.74900271	3.407619	3.102756	AT3G51910	0.208249731	0.19524976	1.19683201	3.602869	6.510375	HSFA7A
AT1G22190	11.232425	3.27800514	0.042334119	1.29170095	0.149037346	0.00000000	0.05758817	0.17819611	0.02312925	4.036429	2.176005	AT1G22190	1.440738292	3.32033926	0.25891353	7.356768	6.212434	AT1G22190
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
AT1G02670	0.50739488	0.115328951	0.000000000	0.122157383	0.000000000	0.000000000	0.000000000	0.00000000	0.000000000	0.187748721	0.082159827	AT1G02670	0.122157383	0.115328951	0.000000000	0.303077672	0.26990855	AT1G02670
AT2G48100	0.51349399	0.046437960	0.042132248	0.000000000	0.028259018	0.039179480	0.049323355	0.00000000	0.049935061	0.144170213	0.114056656	AT2G48100	0.028259018	0.088570208	0.138437896	0.232740422	0.25822687	AT2G48100
EFS	0.49203848	0.007107143	0.106064054	0.043736765	0.031552204	0.009456681	0.034595540	0.00000000	0.013133380	0.053085829	0.193306883	AT1G77300	0.075288969	0.113171197	0.057185601	0.166257026	0.24639271	EFS
EMB2773	0.41785338	0.000000000	0.029724263	0.025483048	0.045004634	0.009416679	0.011973978	0.00000000	0.055577254	0.082000666	0.158672861	AT5G15540	0.070487682	0.029724263	0.076967911	0.111724929	0.24067353	EMB2773
AT2G20110	0.28193951	0.035245793	0.000000000	0.000000000	0.004784689	0.002528828	0.000000000	0.00000000	0.000000000	0.107954546	0.131425650	AT2G20110	0.004784689	0.035245793	0.002528828	0.143200339	0.23938020	AT2G20110
AT4G29000	0.30515594	0.000000000	0.018061780	0.000000000	0.036550357	0.012248714	0.006346626	0.00000000	0.000000000	0.097027757	0.134920705	AT4G29000	0.036550357	0.018061780	0.018595340	0.115089537	0.23194846	AT4G29000
AT2G47090	0.28430413	0.000000000	0.003426918	0.023837312	0.014120941	0.004888947	0.010332741	0.00000000	0.004556113	0.137150745	0.085990417	AT2G47090	0.037958253	0.003426918	0.019777802	0.140577663	0.22314116	AT2G47090
NPR4	0.39091647	0.037197487	0.035836482	0.017601364	0.041798074	0.008335605	0.030750214	0.00000000	0.000000000	0.059228012	0.160169235	AT4G19660	0.059399437	0.073033969	0.039085819	0.132261981	0.21939725	NPR4
TAF6B	0.34252600	0.044182992	0.033528794	0.026843501	0.002870813	0.008486070	0.000000000	0.00000000	0.019324561	0.079484057	0.127805211	AT1G54360	0.029714315	0.077711787	0.027810631	0.157195843	0.20728927	TAF6B
AT1G77250	0.38779598	0.030851146	0.058274008	0.022148446	0.055871591	0.010655722	0.010085824	0.00000000	0.002199011	0.107642409	0.090067819	AT1G77250	0.078020037	0.089125154	0.022940557	0.196767563	0.19771023	AT1G77250
PIE1	0.27238849	0.000000000	0.000000000	0.010404860	0.042726621	0.023477984	0.002159837	0.00000000	0.008577267	0.108566987	0.076474936	AT3G12810	0.053131481	0.000000000	0.034215089	0.108566987	0.18504192	PIE1
FLD	0.35345806	0.000000000	0.009680922	0.000000000	0.073088056	0.033985248	0.014041275	0.00000000	0.038186067	0.077352476	0.107124012	AT3G10390	0.073088056	0.009680922	0.086212590	0.087033398	0.18447649	FLD
AT4G14720	0.30141939	0.000000000	0.000000000	0.000000000	0.024978949	0.013086660	0.032681851	0.00000000	0.053320450	0.074947299	0.102404181	AT4G14720	0.024978949	0.000000000	0.099088961	0.074947299	0.17735148	AT4G14720
LSMT-L	0.27575885	0.060354690	0.005184388	0.017127618	0.000000000	0.000000000	0.000000000	0.01647994	0.000000000	0.124557552	0.052054663	AT1G14030	0.017127618	0.065539077	0.016479937	0.190096629	0.17661221	LSMT-L
AT5G22760	0.31310190	0.000000000	0.031596045	0.023723812	0.037713678	0.020452725	0.007547768	0.00000000	0.022360760	0.067819278	0.101887835	AT5G22760	0.061437490	0.031596045	0.050361253	0.099415323	0.16970711	AT5G22760
AT1G04390	0.24649084	0.037660137	0.000000000	0.013510450	0.027629248	0.004609448	0.002159837	0.00000000	0.000000000	0.071280430	0.089641286	AT1G04390	0.041139699	0.037660137	0.006769286	0.108940567	0.16092172	AT1G04390
BAM7	0.30390126	0.000000000	0.038665951	0.037065681	0.025598128	0.006057172	0.007547768	0.00000000	0.034234295	0.029722733	0.125009535	AT2G45880	0.062663809	0.038665951	0.047839235	0.068388683	0.15473227	BAM7
AT5G41580	0.27954883	0.012683672	0.050472455	0.011529620	0.022196290	0.014368771	0.000000000	0.00000000	0.014053141	0.067952790	0.086292088	AT5G41580	0.033725910	0.063156127	0.028421912	0.131108917	0.15424488	AT5G41580
AT4G21060	0.28943815	0.000000000	0.000000000	0.007674624	0.004784689	0.008295603	0.038774398	0.00000000	0.077396736	0.069275709	0.083236392	AT4G21060	0.012459313	0.000000000	0.124466736	0.069275709	0.15251210	AT4G21060
AT1G29560	0.25467775	0.021845447	0.000000000	0.000000000	0.000000000	0.000000000	0.009723292	0.01979112	0.052965573	0.032763127	0.117589185	AT1G29560	0.000000000	0.021845447	0.082479989	0.054608574	0.15035231	AT1G29560
AT2G24650	0.22585078	0.000000000	0.021326300	0.000000000	0.021029429	0.022047518	0.000000000	0.00000000	0.012977382	0.055891410	0.092578745	AT2G24650	0.021029429	0.021326300	0.035024899	0.077217711	0.14847016	AT2G24650
RAD5	0.24159080	0.022261754	0.015027322	0.000000000	0.000000000	0.028502808	0.012567466	0.00000000	0.026918262	0.080908815	0.055404376	AT5G22750	0.000000000	0.037289076	0.067988535	0.118197892	0.13631319	RAD5
HSI2	0.24652192	0.000000000	0.006853837	0.000000000	0.039053084	0.022836515	0.045426385	0.00000000	0.004556113	0.041436005	0.086359982	AT2G30470	0.039053084	0.006853837	0.072819013	0.048289842	0.12779599	HSI2
AT1G10320	0.21728644	0.000000000	0.000000000	0.005375774	0.009569378	0.016914131	0.018995584	0.00000000	0.040649506	0.067402101	0.058379965	AT1G10320	0.014945152	0.000000000	0.076559221	0.067402101	0.12578207	AT1G10320
HSF1	0.20784229	0.000000000	0.009680922	0.000000000	0.024110166	0.003446658	0.018548885	0.00000000	0.033683929	0.058109745	0.060261982	AT4G17750	0.024110166	0.009680922	0.055679472	0.067790667	0.11837173	HSF1
FRS6	0.16227168	0.000000000	0.011350371	0.000000000	0.021029429	0.007099672	0.019419758	0.00000000	0.002199011	0.025081703	0.076091740	AT1G52520	0.021029429	0.011350371	0.028718442	0.036432073	0.10117344	FRS6
NF-YA9	0.17359013	0.015828651	0.006931972	0.000000000	0.031018654	0.020344792	0.000000000	0.00000000	0.003359876	0.018215405	0.077890784	AT3G20910	0.031018654	0.022760623	0.023704668	0.040976028	0.09610619	NF-YA9
TCP4	0.09707787	0.015658606	0.000000000	0.017648099	0.012440191	0.000000000	0.000000000	0.00000000	0.000000000	0.044436088	0.006894881	AT3G15030	0.030088290	0.015658606	0.000000000	0.060094695	0.05133097	TCP4
AT2G25650	0.03213272	0.000000000	0.000000000	0.000000000	0.004784689	0.000000000	0.000000000	0.00000000	0.000000000	0.011471211	0.015876819	AT2G25650	0.004784689	0.000000000	0.000000000	0.011471211	0.02734803	AT2G25650
AT4G19650	0.02388565	0.000000000	0.000000000	0.000000000	0.003827751	0.006057172	0.000000000	0.00000000	0.000000000	0.008293398	0.005707328	AT4G19650	0.003827751	0.000000000	0.006057172	0.008293398	0.01400073	AT4G19650

options(repr.plot.width=6, repr.plot.height=4)
ggplot(rc_rank[1:10,], aes(x=reorder(GeneName, rc, decreasing = FALSE),  y=rc)) + geom_point(size=4)+ 
  labs(title="Root Cap-specific TF Prioritization",x="", y = "Combined centrality score")+
  theme_classic()+
  theme(axis.text.x = element_text(color = "black", 
                           size = 16, angle = 0),
          axis.text.y = element_text( color = "black", 
                           size = 16, angle = 0),
        plot.title = element_text(color="black", size=18, face="bold"),
        axis.title.x = element_text(color="black", size=16, face="bold"))+ coord_flip()

write.csv(stele_rank,"Root_Cap_TF_centrality_all_transition_zscore3.csv", quote=FALSE, row.names=TRUE)

tf_rank <- rc_rank %>% rownames(.)
# Max 20
p1 <- plot_bc(tf_rank[1]) + plot_bc(tf_rank[2]) + plot_bc(tf_rank[3]) + plot_bc(tf_rank[4]) + plot_bc(tf_rank[5])
p2 <- plot_bc(tf_rank[6]) + plot_bc(tf_rank[7]) + plot_bc(tf_rank[8]) + plot_bc(tf_rank[9]) + plot_bc(tf_rank[10])
p3 <- plot_bc(tf_rank[11]) + plot_bc(tf_rank[12]) + plot_bc(tf_rank[13]) + plot_bc(tf_rank[14]) + plot_bc(tf_rank[15])
p4 <- plot_bc(tf_rank[16]) + plot_bc(tf_rank[17]) + plot_bc(tf_rank[18]) + plot_bc(tf_rank[19]) + plot_bc(tf_rank[20])

p5 <- plot_oc(tf_rank[1]) + plot_oc(tf_rank[2]) + plot_oc(tf_rank[3]) + plot_oc(tf_rank[4]) + plot_oc(tf_rank[5])
p6 <- plot_oc(tf_rank[6]) + plot_oc(tf_rank[7]) + plot_oc(tf_rank[8]) + plot_oc(tf_rank[9]) + plot_oc(tf_rank[10])
p7 <- plot_oc(tf_rank[11]) + plot_oc(tf_rank[12]) + plot_oc(tf_rank[13]) + plot_oc(tf_rank[14]) + plot_oc(tf_rank[15])
p8 <- plot_oc(tf_rank[16]) + plot_oc(tf_rank[17]) + plot_oc(tf_rank[18]) + plot_oc(tf_rank[19]) + plot_oc(tf_rank[20])

p9 <- plot_ic(tf_rank[1]) + plot_ic(tf_rank[2]) + plot_ic(tf_rank[3]) + plot_ic(tf_rank[4]) + plot_ic(tf_rank[5])
p10 <- plot_ic(tf_rank[6]) + plot_ic(tf_rank[7]) + plot_ic(tf_rank[8]) + plot_ic(tf_rank[9]) + plot_ic(tf_rank[10])
p11 <- plot_ic(tf_rank[11]) + plot_ic(tf_rank[12]) + plot_ic(tf_rank[13]) + plot_ic(tf_rank[14]) + plot_ic(tf_rank[15])
p12 <- plot_ic(tf_rank[16]) + plot_ic(tf_rank[17]) + plot_ic(tf_rank[18]) + plot_ic(tf_rank[19]) + plot_ic(tf_rank[20])

p1 <- grid.grabExpr(draw(p1))
p2 <- grid.grabExpr(draw(p2))
p3 <- grid.grabExpr(draw(p3))
p4 <- grid.grabExpr(draw(p4))
p5 <- grid.grabExpr(draw(p5))
p6 <- grid.grabExpr(draw(p6))
p7 <- grid.grabExpr(draw(p7))
p8 <- grid.grabExpr(draw(p8))
p9 <- grid.grabExpr(draw(p9))
p10 <- grid.grabExpr(draw(p10))
p11 <- grid.grabExpr(draw(p11))
p12 <- grid.grabExpr(draw(p12))
col_fun = colorRamp2(c(0, 0.001, 1), c('navy',"khaki1", "red"))
legend_grob1 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "weighted\nnetwork\ncentrality")))
col_fun = colorRamp2(c(0, 0.001, 1), c('gainsboro', "grey", "black"))
legend_grob2 <- grid.grabExpr(draw(Legend(col_fun = col_fun, title = "unweighted\nnetwork\ncentrality")))

options(repr.plot.width=24, repr.plot.height=12)
plot_grid(plot_grid(textbox_grob("Main Regulatory TFs for Root Cap", gp = gpar(fontsize = 20, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),
          plot_grid(plot_grid(textbox_grob("Betweenness centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p1,p2,p3,p4,nrow=4),nrow=2,rel_heights = c(1, 14))
                    ,plot_grid(textbox_grob("Out-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p5,p6,p7,p8,nrow=4),nrow=2,rel_heights=c(1,14))
                    ,plot_grid(textbox_grob("In-degree centrality", gp = gpar(fontsize = 16, col='black'), background_gp = gpar(fill = "#FFFFFF", col = NA)),plot_grid(p9,p10,p11,p12,nrow=4),nrow=2,rel_heights=c(1,14)),nrow=1),nrow=2,rel_heights = c(1, 14)),plot_grid(legend_grob1,legend_grob2,nrow=2), rel_widths = c(19, 1))